From a8fcbd6fb03491bcb693cf43794b5092b26d5506 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Mon, 11 Jan 2021 16:08:42 +0100 Subject: [PATCH 01/43] dmp env wrappers initial --- alr_envs/__init__.py | 18 +- alr_envs/classic_control/hole_reacher.py | 291 +++++++++++++++++++++ alr_envs/classic_control/simple_reacher.py | 2 + alr_envs/utils/dmp_async_vec_env.py | 171 ++++++++++++ alr_envs/utils/dmp_env_wrapper.py | 102 ++++++++ dmp_env_wrapper_example.py | 77 ++++++ example.py | 4 +- 7 files changed, 654 insertions(+), 11 deletions(-) create mode 100644 alr_envs/classic_control/hole_reacher.py create mode 100644 alr_envs/utils/dmp_async_vec_env.py create mode 100644 alr_envs/utils/dmp_env_wrapper.py create mode 100644 dmp_env_wrapper_example.py diff --git a/alr_envs/__init__.py b/alr_envs/__init__.py index 2193db9..9406da2 100644 --- a/alr_envs/__init__.py +++ b/alr_envs/__init__.py @@ -63,15 +63,15 @@ register( } ) -register( - id='ALRReacherSparse-v0', - entry_point='alr_envs.mujoco:ALRReacherEnv', - max_episode_steps=200, - kwargs={ - "steps_before_reward": 200, - "n_links": 7, - } -) +# register( +# id='ALRReacherSparse-v0', +# entry_point='alr_envs.mujoco:ALRReacherEnv', +# max_episode_steps=200, +# kwargs={ +# "steps_before_reward": 200, +# "n_links": 7, +# } +# ) register( id='ALRReacher7Short-v0', diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py new file mode 100644 index 0000000..ddf754b --- /dev/null +++ b/alr_envs/classic_control/hole_reacher.py @@ -0,0 +1,291 @@ +import gym +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import patches + + +def ccw(A, B, C): + return (C[1]-A[1]) * (B[0]-A[0]) - (B[1]-A[1]) * (C[0]-A[0]) > 1e-12 + + +# Return true if line segments AB and CD intersect +def intersect(A, B, C, D): + return ccw(A, C, D) != ccw(B, C, D) and ccw(A, B, C) != ccw(A, B, D) + + +class HoleReacher(gym.Env): + + def __init__(self, num_links, hole_x, hole_width, hole_depth, allow_self_collision=False, + allow_wall_collision=False, collision_penalty=1000): + self.hole_x = hole_x # x-position of center of hole + self.hole_width = hole_width # width of hole + self.hole_depth = hole_depth # depth of hole + self.num_links = num_links + self.link_lengths = np.ones((num_links, 1)) + self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth]) + self.top_center_of_hole = np.hstack([hole_x, 0]) + self.left_wall_edge = np.hstack([hole_x - self.hole_width/2, 0]) + self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0]) + self.allow_self_collision = allow_self_collision + self.allow_wall_collision = allow_wall_collision + self.collision_penalty = collision_penalty + + self._joints = None + self._joint_angles = None + self._angle_velocity = None + self.start_pos = np.hstack([[np.pi/2], np.zeros(self.num_links - 1)]) + self.start_vel = np.zeros(self.num_links) + + self._dt = 0.01 + + action_bound = np.pi * np.ones((self.num_links,)) + state_bound = np.hstack([ + [np.pi] * self.num_links, # cos + [np.pi] * self.num_links, # sin + [np.inf] * self.num_links, # velocity + [np.inf] * 2, # x-y coordinates of target distance + [np.inf] # env steps, because reward start after n steps TODO: Maybe + ]) + self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) + self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) + + self.fig = None + rect_1 = patches.Rectangle((-self.num_links, -1), + self.num_links + self.hole_x - self.hole_width / 2, 1, + fill=True, edgecolor='k', facecolor='k') + rect_2 = patches.Rectangle((self.hole_x + self.hole_width / 2, -1), + self.num_links - self.hole_x + self.hole_width / 2, 1, + fill=True, edgecolor='k', facecolor='k') + rect_3 = patches.Rectangle((self.hole_x - self.hole_width / 2, -1), self.hole_width, + 1 - self.hole_depth, + fill=True, edgecolor='k', facecolor='k') + + self.patches = [rect_1, rect_2, rect_3] + + @property + def end_effector(self): + return self._joints[self.num_links].T + + def step(self, action): + # vel = (action - self._joint_angles) / self._dt + # acc = (vel - self._angle_velocity) / self._dt + # self._angle_velocity = vel + # self._joint_angles = action + + vel = action + acc = (vel - self._angle_velocity) / self._dt + self._angle_velocity = vel + self._joint_angles = self._joint_angles + self._dt * self._angle_velocity + + self._update_joints() + + rew = self._reward() + + rew -= 1e-6 * np.sum(acc**2) + + if self._steps == 180: + rew -= (0.1 * np.sum(vel**2) ** 2 + + 1e-3 * np.sum(action**2) + ) + + if self._is_collided: + rew -= self.collision_penalty + + info = {} + + self._steps += 1 + + return self._get_obs().copy(), rew, self._is_collided, info + + def reset(self): + self._joint_angles = self.start_pos + self._angle_velocity = self.start_vel + self._joints = np.zeros((self.num_links + 1, 2)) + self._update_joints() + self._steps = 0 + + return self._get_obs().copy() + + def _update_joints(self): + """ + update _joints to get new end effector position. The other links are only required for rendering. + Returns: + + """ + line_points_in_taskspace = self.get_forward_kinematics(num_points_per_link=20) + + self._joints[1:, 0] = self._joints[0, 0] + line_points_in_taskspace[:, -1, 0] + self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1] + + self_collision = False + wall_collision = False + + if not self.allow_self_collision: + self_collision = self.check_self_collision(line_points_in_taskspace) + if np.any(np.abs(self._joint_angles) > np.pi) and not self.allow_self_collision: + self_collision = True + + if not self.allow_wall_collision: + wall_collision = self.check_wall_collision(line_points_in_taskspace) + + self._is_collided = self_collision or wall_collision + + def _get_obs(self): + theta = self._joint_angles + return np.hstack([ + np.cos(theta), + np.sin(theta), + self._angle_velocity, + self.end_effector - self.bottom_center_of_hole, + self._steps + ]) + + def _reward(self): + dist_reward = 0 + if not self._is_collided: + if self._steps == 180: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + else: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + + # TODO: make negative + out = - dist_reward ** 2 + + return out + + def get_forward_kinematics(self, num_points_per_link=1): + theta = self._joint_angles[:, None] + + if num_points_per_link > 1: + intermediate_points = np.linspace(0, 1, num_points_per_link) + else: + intermediate_points = 1 + + accumulated_theta = np.cumsum(theta, axis=0) + + endeffector = np.zeros(shape=(self.num_links, num_points_per_link, 2)) + + x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points + y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points + + endeffector[0, :, 0] = x[0, :] + endeffector[0, :, 1] = y[0, :] + + for i in range(1, self.num_links): + endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0] + endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] + + return np.squeeze(endeffector + self._joints[0, :]) + + def check_self_collision(self, line_points): + for i, line1 in enumerate(line_points): + for line2 in line_points[i+2:, :, :]: + # if line1 != line2: + if intersect(line1[0], line1[-1], line2[0], line2[-1]): + return True + return False + + def check_wall_collision(self, line_points): + + # all points that are before the hole in x + r, c = np.where(line_points[:, :, 0] < (self.hole_x - self.hole_width / 2)) + + # check if any of those points are below surface + nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0) + + if nr_line_points_below_surface_before_hole > 0: + return True + + # all points that are after the hole in x + r, c = np.where(line_points[:, :, 0] > (self.hole_x + self.hole_width / 2)) + + # check if any of those points are below surface + nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0) + + if nr_line_points_below_surface_after_hole > 0: + return True + + # all points that are above the hole + r, c = np.where((line_points[:, :, 0] > (self.hole_x - self.hole_width / 2)) & ( + line_points[:, :, 0] < (self.hole_x + self.hole_width / 2))) + + # check if any of those points are below surface + nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self.hole_depth) + + if nr_line_points_below_surface_in_hole > 0: + return True + + return False + + def render(self, mode='human'): + if self.fig is None: + self.fig = plt.figure() + plt.ion() + plt.pause(0.01) + else: + plt.figure(self.fig.number) + + if mode == "human": + plt.cla() + plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}") + + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + + lim = np.sum(self.link_lengths) + 0.5 + plt.xlim([-lim, lim]) + plt.ylim([-1.1, lim]) + # plt.draw() + plt.pause(1e-4) # pushes window to foreground, which is annoying. + # self.fig.canvas.flush_events() + + elif render_partial: + if t == 0: + fig, ax = plt.subplots() + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + + plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links) + + if t % 20 == 0 or t == 199 or is_collided: + ax.plot(line_points_in_taskspace[:, 0, 0], + line_points_in_taskspace[:, 0, 1], + line_points_in_taskspace[:, -1, 0], + line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200) + + plt.pause(0.01) + + elif render_final: + if self._steps == 0 or self._is_collided: + fig, ax = plt.subplots() + + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + + plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links) + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + + plt.pause(0.01) + + +if __name__ == '__main__': + nl = 5 + env = HoleReacher(num_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15, hole_depth=1, hole_x=1) + env.reset() + + for i in range(200): + # objective.load_result("/tmp/cma") + # explore = np.random.multivariate_normal(mean=np.zeros(30), cov=1 * np.eye(30)) + ac = 11 * env.action_space.sample() + # ac[0] += np.pi/2 + obs, rew, done, info = env.step(ac) + env.render(mode="render_full") + + print(rew) + + if done: + break diff --git a/alr_envs/classic_control/simple_reacher.py b/alr_envs/classic_control/simple_reacher.py index 042b207..c7e6ddb 100644 --- a/alr_envs/classic_control/simple_reacher.py +++ b/alr_envs/classic_control/simple_reacher.py @@ -6,6 +6,8 @@ import matplotlib.pyplot as plt import numpy as np from gym import spaces from gym.utils import seeding +from alr_envs.utils.utils import angle_normalize + if os.environ.get("DISPLAY", None): mpl.use('Qt5Agg') diff --git a/alr_envs/utils/dmp_async_vec_env.py b/alr_envs/utils/dmp_async_vec_env.py new file mode 100644 index 0000000..3cf48af --- /dev/null +++ b/alr_envs/utils/dmp_async_vec_env.py @@ -0,0 +1,171 @@ +import gym +from gym.error import (AlreadyPendingCallError, NoAsyncCallError) +from gym.vector.utils import concatenate, create_empty_array +from gym.vector.async_vector_env import AsyncState +import numpy as np +import multiprocessing as mp +from copy import deepcopy +import sys + + +class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): + def __init__(self, env_fns, n_samples, observation_space=None, action_space=None, + shared_memory=True, copy=True, context=None, daemon=True, worker=None): + super(DmpAsyncVectorEnv, self).__init__(env_fns, + observation_space=observation_space, + action_space=action_space, + shared_memory=shared_memory, + copy=copy, + context=context, + daemon=daemon, + worker=worker) + + # we need to overwrite the number of samples as we may sample more than num_envs + self.observations = create_empty_array(self.single_observation_space, + n=n_samples, + fn=np.zeros) + + def __call__(self, params): + return self.rollout(params) + + def rollout_async(self, actions): + """ + Parameters + ---------- + actions : iterable of samples from `action_space` + List of actions. + """ + self._assert_is_running() + if self._state != AsyncState.DEFAULT: + raise AlreadyPendingCallError('Calling `rollout_async` while waiting ' + 'for a pending call to `{0}` to complete.'.format( + self._state.value), self._state.value) + + # split_actions = np.array_split(actions, self.num_envs) + actions = np.atleast_2d(actions) + split_actions = np.array_split(actions, np.minimum(len(actions), self.num_envs)) + for pipe, action in zip(self.parent_pipes, split_actions): + pipe.send(('rollout', action)) + for pipe in self.parent_pipes[len(split_actions):]: + pipe.send(('idle', None)) + self._state = AsyncState.WAITING_ROLLOUT + + def rollout_wait(self, timeout=None): + """ + Parameters + ---------- + timeout : int or float, optional + Number of seconds before the call to `step_wait` times out. If + `None`, the call to `step_wait` never times out. + + Returns + ------- + observations : sample from `observation_space` + A batch of observations from the vectorized environment. + + rewards : `np.ndarray` instance (dtype `np.float_`) + A vector of rewards from the vectorized environment. + + dones : `np.ndarray` instance (dtype `np.bool_`) + A vector whose entries indicate whether the episode has ended. + + infos : list of dict + A list of auxiliary diagnostic information. + """ + self._assert_is_running() + if self._state != AsyncState.WAITING_ROLLOUT: + raise NoAsyncCallError('Calling `rollout_wait` without any prior call ' + 'to `rollout_async`.', AsyncState.WAITING_ROLLOUT.value) + + if not self._poll(timeout): + self._state = AsyncState.DEFAULT + raise mp.TimeoutError('The call to `rollout_wait` has timed out after ' + '{0} second{1}.'.format(timeout, 's' if timeout > 1 else '')) + + results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes]) + results = [r for r in results if r is not None] + self._raise_if_errors(successes) + self._state = AsyncState.DEFAULT + + observations_list, rewards, dones, infos = [_flatten_list(r) for r in zip(*results)] + + # if not self.shared_memory: + # self.observations = concatenate(observations_list, self.observations, + # self.single_observation_space) + + # return (deepcopy(self.observations) if self.copy else self.observations, + # np.array(rewards), np.array(dones, dtype=np.bool_), infos) + + return np.array(rewards) + + def rollout(self, actions): + self.rollout_async(actions) + return self.rollout_wait() + + +def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): + assert shared_memory is None + env = env_fn() + parent_pipe.close() + try: + while True: + command, data = pipe.recv() + if command == 'reset': + observation = env.reset() + pipe.send((observation, True)) + elif command == 'step': + observation, reward, done, info = env.step(data) + if done: + observation = env.reset() + pipe.send(((observation, reward, done, info), True)) + elif command == 'rollout': + observations = [] + rewards = [] + dones = [] + infos = [] + for d in data: + env.reset() + observation, reward, done, info = env.step(d) + observations.append(observation) + rewards.append(reward) + dones.append(done) + infos.append(info) + pipe.send(((observations, rewards, dones, infos), (True, ) * len(rewards))) + elif command == 'seed': + env.seed(data) + pipe.send((None, True)) + elif command == 'close': + pipe.send((None, True)) + break + elif command == 'idle': + pipe.send((None, True)) + elif command == '_check_observation_space': + pipe.send((data == env.observation_space, True)) + else: + raise RuntimeError('Received unknown command `{0}`. Must ' + 'be one of {`reset`, `step`, `seed`, `close`, ' + '`_check_observation_space`}.'.format(command)) + except (KeyboardInterrupt, Exception): + error_queue.put((index,) + sys.exc_info()[:2]) + pipe.send((None, False)) + finally: + env.close() + + +def _flatten_obs(obs): + assert isinstance(obs, (list, tuple)) + assert len(obs) > 0 + + if isinstance(obs[0], dict): + keys = obs[0].keys() + return {k: np.stack([o[k] for o in obs]) for k in keys} + else: + return np.stack(obs) + + +def _flatten_list(l): + assert isinstance(l, (list, tuple)) + assert len(l) > 0 + assert all([len(l_) > 0 for l_ in l]) + + return [l__ for l_ in l for l__ in l_] diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py new file mode 100644 index 0000000..63d09ce --- /dev/null +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -0,0 +1,102 @@ +from mp_lib.phase import ExpDecayPhaseGenerator +from mp_lib.basis import DMPBasisGenerator +from mp_lib import dmps +import numpy as np +import gym + + +class DmpEnvWrapperBase(gym.Wrapper): + def __init__(self, env, num_dof, num_basis, duration=1, dt=0.01, learn_goal=False): + super(DmpEnvWrapperBase, self).__init__(env) + self.num_dof = num_dof + self.num_basis = num_basis + self.dim = num_dof * num_basis + if learn_goal: + self.dim += num_dof + self.learn_goal = True + duration = duration # seconds + time_steps = int(duration / dt) + self.t = np.linspace(0, duration, time_steps) + + phase_generator = ExpDecayPhaseGenerator(alpha_phase=5, duration=duration) + basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=self.num_basis) + + self.dmp = dmps.DMP(num_dof=num_dof, + basis_generator=basis_generator, + phase_generator=phase_generator, + num_time_steps=time_steps, + dt=dt + ) + + self.dmp.dmp_start_pos = env.start_pos.reshape((1, num_dof)) + + dmp_weights = np.zeros((num_basis, num_dof)) + dmp_goal_pos = np.zeros(num_dof) + + self.dmp.set_weights(dmp_weights, dmp_goal_pos) + + def goal_and_weights(self, params): + if len(params.shape) > 1: + assert params.shape[1] == self.dim + else: + assert len(params) == self.dim + params = np.reshape(params, [1, self.dim]) + + if self.learn_goal: + goal_pos = params[0, -self.num_dof:] + weight_matrix = np.reshape(params[:, :-self.num_dof], [self.num_basis, self.num_dof]) + else: + goal_pos = None + weight_matrix = np.reshape(params, [self.num_basis, self.num_dof]) + + return goal_pos, weight_matrix + + def step(self, action, render=False): + """ overwrite step function where action now is the weights and possible goal position""" + raise NotImplementedError + + +class DmpEnvWrapperAngle(DmpEnvWrapperBase): + def step(self, action, render=False): + goal_pos, weight_matrix = self.goal_and_weights(action) + self.dmp.set_weights(weight_matrix, goal_pos) + trajectory, velocities = self.dmp.reference_trajectory(self.t) + + rews = [] + + for t, traj in enumerate(trajectory): + obs, rew, done, info = self.env.step(traj) + rews.append(rew) + if render: + self.env.render(mode="human") + if done: + break + + reward = np.sum(rews) + done = True + info = {} + + return obs, reward, done, info + + +class DmpEnvWrapperVel(DmpEnvWrapperBase): + def step(self, action, render=False): + goal_pos, weight_matrix = self.goal_and_weights(action) + weight_matrix *= 50 + self.dmp.set_weights(weight_matrix, goal_pos) + trajectory, velocities = self.dmp.reference_trajectory(self.t) + + rews = [] + + for t, vel in enumerate(velocities): + obs, rew, done, info = self.env.step(vel) + rews.append(rew) + if render: + self.env.render(mode="human") + if done: + break + + reward = np.sum(rews) + info = {} + + return obs, reward, done, info diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py new file mode 100644 index 0000000..9d699be --- /dev/null +++ b/dmp_env_wrapper_example.py @@ -0,0 +1,77 @@ +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperVel +from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker +from alr_envs.classic_control.hole_reacher import HoleReacher +from gym.vector.async_vector_env import AsyncVectorEnv +import numpy as np + + +# env = gym.make('alr_envs:SimpleReacher-v0') +# env = HoleReacher(num_links=5, +# allow_self_collision=False, +# allow_wall_collision=True, +# hole_width=0.15, +# hole_depth=1, +# hole_x=1) +# +# env = DmpEnvWrapperVel(env, +# num_dof=5, +# num_basis=5, +# duration=2, +# dt=env._dt, +# learn_goal=True) +# +# params = np.hstack([50 * np.random.randn(25), np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4])]) +# +# print(params) +# +# env.reset() +# obs, rew, done, info = env.step(params, render=True) +# +# print(env.env._joint_angles) +# +# print(rew) + +if __name__ == "__main__": + + def make_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the inital seed for RNG + :param rank: (int) index of the subprocess + """ + def _init(): + env = HoleReacher(num_links=5, + allow_self_collision=False, + allow_wall_collision=False, + hole_width=0.15, + hole_depth=1, + hole_x=1) + + env = DmpEnvWrapperVel(env, + num_dof=5, + num_basis=5, + duration=2, + dt=env._dt, + learn_goal=True) + env.seed(seed + rank) + return env + return _init + + n_samples = 4 + + env = DmpAsyncVectorEnv([make_env(i) for i in range(4)], + n_samples=n_samples, + context="spawn", + shared_memory=False, + worker=_worker) + + # params = np.random.randn(4, 25) + params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) + + # env.reset() + out = env.rollout(params) + + print(out) diff --git a/example.py b/example.py index fd11c73..cc96611 100644 --- a/example.py +++ b/example.py @@ -4,8 +4,8 @@ import gym if __name__ == '__main__': - env = gym.make('alr_envs:ALRReacher-v0') - # env = gym.make('alr_envs:SimpleReacher-v0') + # env = gym.make('alr_envs:ALRReacher-v0') + env = gym.make('alr_envs:SimpleReacher-v0') # env = gym.make('alr_envs:ALRReacher7-v0') state = env.reset() From 104281fe16b561a3e203f9a7445fe3bc3038a3a3 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Tue, 12 Jan 2021 10:52:08 +0100 Subject: [PATCH 02/43] changed from step to rollout method --- alr_envs/classic_control/hole_reacher.py | 139 +++++++++++++---------- alr_envs/utils/dmp_async_vec_env.py | 7 +- alr_envs/utils/dmp_env_wrapper.py | 42 +++++-- setup.py | 2 +- 4 files changed, 117 insertions(+), 73 deletions(-) diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index ddf754b..441e0cf 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -1,5 +1,7 @@ import gym import numpy as np +import matplotlib +matplotlib.use('TkAgg') import matplotlib.pyplot as plt from matplotlib import patches @@ -35,6 +37,7 @@ class HoleReacher(gym.Env): self._angle_velocity = None self.start_pos = np.hstack([[np.pi/2], np.zeros(self.num_links - 1)]) self.start_vel = np.zeros(self.num_links) + self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer self._dt = 0.01 @@ -66,37 +69,6 @@ class HoleReacher(gym.Env): def end_effector(self): return self._joints[self.num_links].T - def step(self, action): - # vel = (action - self._joint_angles) / self._dt - # acc = (vel - self._angle_velocity) / self._dt - # self._angle_velocity = vel - # self._joint_angles = action - - vel = action - acc = (vel - self._angle_velocity) / self._dt - self._angle_velocity = vel - self._joint_angles = self._joint_angles + self._dt * self._angle_velocity - - self._update_joints() - - rew = self._reward() - - rew -= 1e-6 * np.sum(acc**2) - - if self._steps == 180: - rew -= (0.1 * np.sum(vel**2) ** 2 - + 1e-3 * np.sum(action**2) - ) - - if self._is_collided: - rew -= self.collision_penalty - - info = {} - - self._steps += 1 - - return self._get_obs().copy(), rew, self._is_collided, info - def reset(self): self._joint_angles = self.start_pos self._angle_velocity = self.start_vel @@ -106,6 +78,46 @@ class HoleReacher(gym.Env): return self._get_obs().copy() + def step(self, action): + """ + a single step with an action in joint velocity space + """ + vel = action + acc = (vel - self._angle_velocity) / self._dt + self._angle_velocity = vel + self._joint_angles = self._joint_angles + self._dt * self._angle_velocity + + self._update_joints() + + # rew = self._reward() + + # compute reward directly in step function + + dist_reward = 0 + if not self._is_collided: + if self._steps == 180: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + else: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + + reward = - dist_reward ** 2 + + reward -= 1e-6 * np.sum(acc**2) + + if self._steps == 180: + reward -= (0.1 * np.sum(vel**2) ** 2 + + 1e-3 * np.sum(action**2) + ) + + if self._is_collided: + reward -= self.collision_penalty + + info = {} + + self._steps += 1 + + return self._get_obs().copy(), reward, self._is_collided, info + def _update_joints(self): """ update _joints to get new end effector position. The other links are only required for rendering. @@ -140,18 +152,17 @@ class HoleReacher(gym.Env): self._steps ]) - def _reward(self): - dist_reward = 0 - if not self._is_collided: - if self._steps == 180: - dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) - else: - dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) - - # TODO: make negative - out = - dist_reward ** 2 - - return out + # def _reward(self): + # dist_reward = 0 + # if not self._is_collided: + # if self._steps == 180: + # dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + # else: + # dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + # + # out = - dist_reward ** 2 + # + # return out def get_forward_kinematics(self, num_points_per_link=1): theta = self._joint_angles[:, None] @@ -220,8 +231,8 @@ class HoleReacher(gym.Env): def render(self, mode='human'): if self.fig is None: self.fig = plt.figure() - plt.ion() - plt.pause(0.01) + # plt.ion() + # plt.pause(0.01) else: plt.figure(self.fig.number) @@ -242,25 +253,29 @@ class HoleReacher(gym.Env): plt.pause(1e-4) # pushes window to foreground, which is annoying. # self.fig.canvas.flush_events() - elif render_partial: - if t == 0: - fig, ax = plt.subplots() + elif mode == "partial": + if self._steps == 1: + # fig, ax = plt.subplots() # Add the patch to the Axes [plt.gca().add_patch(rect) for rect in self.patches] + # plt.pause(0.01) - plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links) - - if t % 20 == 0 or t == 199 or is_collided: - ax.plot(line_points_in_taskspace[:, 0, 0], - line_points_in_taskspace[:, 0, 1], - line_points_in_taskspace[:, -1, 0], - line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200) + if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided: + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', alpha=self._steps / 200) + # ax.plot(line_points_in_taskspace[:, 0, 0], + # line_points_in_taskspace[:, 0, 1], + # line_points_in_taskspace[:, -1, 0], + # line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200) + lim = np.sum(self.link_lengths) + 0.5 + plt.xlim([-lim, lim]) + plt.ylim([-1.1, lim]) plt.pause(0.01) - elif render_final: - if self._steps == 0 or self._is_collided: - fig, ax = plt.subplots() + elif mode == "final": + if self._steps == 199 or self._is_collided: + # fig, ax = plt.subplots() # Add the patch to the Axes [plt.gca().add_patch(rect) for rect in self.patches] @@ -274,16 +289,18 @@ class HoleReacher(gym.Env): if __name__ == '__main__': nl = 5 + render_mode = "human" # "human" or "partial" or "final" env = HoleReacher(num_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15, hole_depth=1, hole_x=1) env.reset() + # env.render(mode=render_mode) for i in range(200): # objective.load_result("/tmp/cma") - # explore = np.random.multivariate_normal(mean=np.zeros(30), cov=1 * np.eye(30)) - ac = 11 * env.action_space.sample() + # test with random actions + ac = 2 * env.action_space.sample() # ac[0] += np.pi/2 obs, rew, done, info = env.step(ac) - env.render(mode="render_full") + env.render(mode=render_mode) print(rew) diff --git a/alr_envs/utils/dmp_async_vec_env.py b/alr_envs/utils/dmp_async_vec_env.py index 3cf48af..f576277 100644 --- a/alr_envs/utils/dmp_async_vec_env.py +++ b/alr_envs/utils/dmp_async_vec_env.py @@ -4,7 +4,6 @@ from gym.vector.utils import concatenate, create_empty_array from gym.vector.async_vector_env import AsyncState import numpy as np import multiprocessing as mp -from copy import deepcopy import sys @@ -41,7 +40,6 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): 'for a pending call to `{0}` to complete.'.format( self._state.value), self._state.value) - # split_actions = np.array_split(actions, self.num_envs) actions = np.atleast_2d(actions) split_actions = np.array_split(actions, np.minimum(len(actions), self.num_envs)) for pipe, action in zip(self.parent_pipes, split_actions): @@ -89,6 +87,8 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): observations_list, rewards, dones, infos = [_flatten_list(r) for r in zip(*results)] + # for now, we ignore the observations and only return the rewards + # if not self.shared_memory: # self.observations = concatenate(observations_list, self.observations, # self.single_observation_space) @@ -124,8 +124,7 @@ def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): dones = [] infos = [] for d in data: - env.reset() - observation, reward, done, info = env.step(d) + observation, reward, done, info = env.rollout(d) observations.append(observation) rewards.append(reward) dones.append(done) diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 63d09ce..cf73ae5 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -14,7 +14,7 @@ class DmpEnvWrapperBase(gym.Wrapper): if learn_goal: self.dim += num_dof self.learn_goal = True - duration = duration # seconds + self.duration = duration # seconds time_steps = int(duration / dt) self.t = np.linspace(0, duration, time_steps) @@ -35,6 +35,21 @@ class DmpEnvWrapperBase(gym.Wrapper): self.dmp.set_weights(dmp_weights, dmp_goal_pos) + def __call__(self, params): + params = np.atleast_2d(params) + observations = [] + rewards = [] + dones = [] + infos = [] + for p in params: + observation, reward, done, info = self.rollout(p) + observations.append(observation) + rewards.append(reward) + dones.append(done) + infos.append(info) + + return np.array(rewards) + def goal_and_weights(self, params): if len(params.shape) > 1: assert params.shape[1] == self.dim @@ -51,19 +66,26 @@ class DmpEnvWrapperBase(gym.Wrapper): return goal_pos, weight_matrix - def step(self, action, render=False): - """ overwrite step function where action now is the weights and possible goal position""" + def rollout(self, params, render=False): + """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" raise NotImplementedError class DmpEnvWrapperAngle(DmpEnvWrapperBase): - def step(self, action, render=False): + """ + Wrapper for gym environments which creates a trajectory in joint angle space + """ + def rollout(self, action, render=False): goal_pos, weight_matrix = self.goal_and_weights(action) + if hasattr(self.env, "weight_matrix_scale"): + weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) trajectory, velocities = self.dmp.reference_trajectory(self.t) rews = [] + self.env.reset() + for t, traj in enumerate(trajectory): obs, rew, done, info = self.env.step(traj) rews.append(rew) @@ -73,21 +95,27 @@ class DmpEnvWrapperAngle(DmpEnvWrapperBase): break reward = np.sum(rews) - done = True + # done = True info = {} return obs, reward, done, info class DmpEnvWrapperVel(DmpEnvWrapperBase): - def step(self, action, render=False): + """ + Wrapper for gym environments which creates a trajectory in joint velocity space + """ + def rollout(self, action, render=False): goal_pos, weight_matrix = self.goal_and_weights(action) - weight_matrix *= 50 + if hasattr(self.env, "weight_matrix_scale"): + weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) trajectory, velocities = self.dmp.reference_trajectory(self.t) rews = [] + self.env.reset() + for t, vel in enumerate(velocities): obs, rew, done, info = self.env.step(vel) rews.append(rew) diff --git a/setup.py b/setup.py index 81f949d..6f30786 100644 --- a/setup.py +++ b/setup.py @@ -2,5 +2,5 @@ from setuptools import setup setup(name='alr_envs', version='0.0.1', - install_requires=['gym', 'PyQt5'] # And any other dependencies foo needs + install_requires=['gym', 'PyQt5', 'matplotlib'] # And any other dependencies foo needs ) From b7400c477d383108887fb4821f40928b43897a9a Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 14 Jan 2021 17:10:03 +0100 Subject: [PATCH 03/43] updates --- alr_envs/classic_control/hole_reacher.py | 10 ++++--- alr_envs/classic_control/utils.py | 34 ++++++++++++++++++++++++ alr_envs/utils/dmp_async_vec_env.py | 3 ++- alr_envs/utils/dmp_env_wrapper.py | 3 ++- setup.py | 3 ++- 5 files changed, 47 insertions(+), 6 deletions(-) create mode 100644 alr_envs/classic_control/utils.py diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index 441e0cf..f19915b 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -1,7 +1,5 @@ import gym import numpy as np -import matplotlib -matplotlib.use('TkAgg') import matplotlib.pyplot as plt from matplotlib import patches @@ -112,7 +110,7 @@ class HoleReacher(gym.Env): if self._is_collided: reward -= self.collision_penalty - info = {} + info = {"is_collided": self._is_collided} self._steps += 1 @@ -286,6 +284,10 @@ class HoleReacher(gym.Env): plt.pause(0.01) + def close(self): + if self.fig is not None: + plt.close(self.fig) + if __name__ == '__main__': nl = 5 @@ -306,3 +308,5 @@ if __name__ == '__main__': if done: break + + env.close() diff --git a/alr_envs/classic_control/utils.py b/alr_envs/classic_control/utils.py new file mode 100644 index 0000000..61156f1 --- /dev/null +++ b/alr_envs/classic_control/utils.py @@ -0,0 +1,34 @@ +from alr_envs.classic_control.hole_reacher import HoleReacher +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperVel + + +def make_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the initial seed for RNG + :param rank: (int) index of the subprocess + :returns a function that generates an environment + """ + + def _init(): + env = HoleReacher(num_links=5, + allow_self_collision=False, + allow_wall_collision=False, + hole_width=0.15, + hole_depth=1, + hole_x=1, + collision_penalty=100000) + + env = DmpEnvWrapperVel(env, + num_dof=5, + num_basis=5, + duration=2, + dt=env._dt, + learn_goal=True) + env.seed(seed + rank) + return env + + return _init diff --git a/alr_envs/utils/dmp_async_vec_env.py b/alr_envs/utils/dmp_async_vec_env.py index f576277..56f4ca7 100644 --- a/alr_envs/utils/dmp_async_vec_env.py +++ b/alr_envs/utils/dmp_async_vec_env.py @@ -96,7 +96,7 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): # return (deepcopy(self.observations) if self.copy else self.observations, # np.array(rewards), np.array(dones, dtype=np.bool_), infos) - return np.array(rewards) + return np.array(rewards), infos def rollout(self, actions): self.rollout_async(actions) @@ -134,6 +134,7 @@ def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): env.seed(data) pipe.send((None, True)) elif command == 'close': + env.close() pipe.send((None, True)) break elif command == 'idle': diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index cf73ae5..9b1bdf1 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -113,18 +113,19 @@ class DmpEnvWrapperVel(DmpEnvWrapperBase): trajectory, velocities = self.dmp.reference_trajectory(self.t) rews = [] + infos = [] self.env.reset() for t, vel in enumerate(velocities): obs, rew, done, info = self.env.step(vel) rews.append(rew) + infos.append(info) if render: self.env.render(mode="human") if done: break reward = np.sum(rews) - info = {} return obs, reward, done, info diff --git a/setup.py b/setup.py index 6f30786..88324de 100644 --- a/setup.py +++ b/setup.py @@ -2,5 +2,6 @@ from setuptools import setup setup(name='alr_envs', version='0.0.1', - install_requires=['gym', 'PyQt5', 'matplotlib'] # And any other dependencies foo needs + install_requires=['gym', 'PyQt5', 'matplotlib', + 'mp_lib @ git+https://git@github.com/maxhuettenrauch/mp_lib@master#egg=mp_lib',], # And any other dependencies foo needs ) From 2d9e7fb3eb4fa9cf894efe3dc3603b170a643151 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Fri, 15 Jan 2021 17:16:52 +0100 Subject: [PATCH 04/43] fixes in holereacher --- alr_envs/classic_control/hole_reacher.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index f19915b..0db772a 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -38,6 +38,7 @@ class HoleReacher(gym.Env): self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer self._dt = 0.01 + self.time_limit = 2 action_bound = np.pi * np.ones((self.num_links,)) state_bound = np.hstack([ @@ -103,9 +104,7 @@ class HoleReacher(gym.Env): reward -= 1e-6 * np.sum(acc**2) if self._steps == 180: - reward -= (0.1 * np.sum(vel**2) ** 2 - + 1e-3 * np.sum(action**2) - ) + reward -= 0.1 * np.sum(vel**2) ** 2 if self._is_collided: reward -= self.collision_penalty @@ -114,7 +113,9 @@ class HoleReacher(gym.Env): self._steps += 1 - return self._get_obs().copy(), reward, self._is_collided, info + done = self._steps * self._dt > self.time_limit or self._is_collided + + return self._get_obs().copy(), reward, done, info def _update_joints(self): """ @@ -301,12 +302,12 @@ if __name__ == '__main__': # test with random actions ac = 2 * env.action_space.sample() # ac[0] += np.pi/2 - obs, rew, done, info = env.step(ac) + obs, rew, d, info = env.step(ac) env.render(mode=render_mode) print(rew) - if done: + if d: break env.close() From cab2c249bb8ac9ac30113150cec0dfbe6b333532 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 21 Jan 2021 09:42:04 +0100 Subject: [PATCH 05/43] started table tennis env --- alr_envs/mujoco/gym_table_tennis/__init__.py | 1 + .../mujoco/gym_table_tennis/envs/__init__.py | 1 + .../table_tennis/include_7_motor_actuator.xml | 12 + .../include_barrett_wam_7dof_left.xml | 76 ++++ .../include_barrett_wam_7dof_right.xml | 95 +++++ .../assets/table_tennis/include_table.xml | 38 ++ .../table_tennis/include_target_ball.xml | 10 + .../table_tennis/include_test_balls.xml | 80 ++++ .../table_tennis/meshes/base_link_convex.stl | Bin 0 -> 16584 bytes .../table_tennis/meshes/base_link_fine.stl | Bin 0 -> 720584 bytes .../meshes/bhand_finger_dist_link_convex.stl | Bin 0 -> 57684 bytes .../meshes/bhand_finger_dist_link_fine.stl | Bin 0 -> 410684 bytes .../meshes/bhand_finger_med_link_convex.stl | Bin 0 -> 16084 bytes .../meshes/bhand_finger_med_link_fine.stl | Bin 0 -> 87484 bytes ...nger_prox_link_convex_decomposition_p1.stl | Bin 0 -> 7084 bytes ...nger_prox_link_convex_decomposition_p2.stl | Bin 0 -> 86684 bytes ...nger_prox_link_convex_decomposition_p3.stl | Bin 0 -> 4584 bytes .../meshes/bhand_finger_prox_link_fine.stl | Bin 0 -> 235784 bytes .../table_tennis/meshes/bhand_palm_fine.stl | Bin 0 -> 1146484 bytes ...hand_palm_link_convex_decomposition_p1.stl | Bin 0 -> 96584 bytes ...hand_palm_link_convex_decomposition_p2.stl | Bin 0 -> 59384 bytes ...hand_palm_link_convex_decomposition_p3.stl | Bin 0 -> 77684 bytes ...hand_palm_link_convex_decomposition_p4.stl | Bin 0 -> 6884 bytes .../table_tennis/meshes/elbow_link_convex.stl | Bin 0 -> 16484 bytes .../table_tennis/meshes/elbow_link_fine.stl | Bin 0 -> 141384 bytes .../forearm_link_convex_decomposition_p1.stl | Bin 0 -> 33484 bytes .../forearm_link_convex_decomposition_p2.stl | Bin 0 -> 205484 bytes .../table_tennis/meshes/forearm_link_fine.stl | Bin 0 -> 154484 bytes .../shoulder_link_convex_decomposition_p1.stl | Bin 0 -> 23284 bytes .../shoulder_link_convex_decomposition_p2.stl | Bin 0 -> 60284 bytes .../shoulder_link_convex_decomposition_p3.stl | Bin 0 -> 56284 bytes .../meshes/shoulder_link_fine.stl | Bin 0 -> 362984 bytes .../meshes/shoulder_pitch_link_convex.stl | Bin 0 -> 24284 bytes .../meshes/shoulder_pitch_link_fine.stl | Bin 0 -> 146084 bytes ...upper_arm_link_convex_decomposition_p1.stl | Bin 0 -> 104884 bytes ...upper_arm_link_convex_decomposition_p2.stl | Bin 0 -> 13484 bytes .../meshes/upper_arm_link_fine.stl | Bin 0 -> 615684 bytes .../meshes/wrist_palm_link_convex.stl | Bin 0 -> 7684 bytes .../meshes/wrist_palm_link_fine.stl | Bin 0 -> 44484 bytes ...ist_pitch_link_convex_decomposition_p1.stl | Bin 0 -> 35284 bytes ...ist_pitch_link_convex_decomposition_p2.stl | Bin 0 -> 34284 bytes ...ist_pitch_link_convex_decomposition_p3.stl | Bin 0 -> 33284 bytes .../meshes/wrist_pitch_link_fine.stl | Bin 0 -> 563684 bytes ...wrist_yaw_link_convex_decomposition_p1.stl | Bin 0 -> 19284 bytes ...wrist_yaw_link_convex_decomposition_p2.stl | Bin 0 -> 63684 bytes .../meshes/wrist_yaw_link_fine.stl | Bin 0 -> 1316784 bytes .../table_tennis/right_arm_actuator.xml | 19 + .../robotics/assets/table_tennis/shared.xml | 49 +++ .../assets/table_tennis/table_tennis_env.xml | 41 ++ .../gym_table_tennis/envs/table_tennis_env.py | 233 ++++++++++ .../mujoco/gym_table_tennis/utils/__init__.py | 0 .../gym_table_tennis/utils/experiment.py | 83 ++++ .../utils/rewards/__init__.py | 0 .../utils/rewards/hierarchical_reward.py | 402 ++++++++++++++++++ .../gym_table_tennis/utils/rewards/rewards.py | 136 ++++++ .../mujoco/gym_table_tennis/utils/util.py | 49 +++ example.py | 4 +- 57 files changed, 1327 insertions(+), 2 deletions(-) create mode 100644 alr_envs/mujoco/gym_table_tennis/__init__.py create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/__init__.py create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_table.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_target_ball.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_test_balls.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_dist_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_dist_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_med_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_med_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_prox_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_prox_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_prox_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_finger_prox_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_palm_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_palm_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_palm_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_palm_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/bhand_palm_link_convex_decomposition_p4.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/elbow_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/elbow_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/__init__.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/experiment.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/rewards/__init__.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/rewards/hierarchical_reward.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/rewards/rewards.py create mode 100644 alr_envs/mujoco/gym_table_tennis/utils/util.py diff --git a/alr_envs/mujoco/gym_table_tennis/__init__.py b/alr_envs/mujoco/gym_table_tennis/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/__init__.py @@ -0,0 +1 @@ + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/__init__.py b/alr_envs/mujoco/gym_table_tennis/envs/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/__init__.py @@ -0,0 +1 @@ + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml new file mode 100644 index 0000000..7772d14 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml new file mode 100644 index 0000000..a8df915 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml @@ -0,0 +1,76 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml new file mode 100644 index 0000000..011b95a --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_table.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_table.xml new file mode 100644 index 0000000..ad1ae35 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_table.xml @@ -0,0 +1,38 @@ + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_target_ball.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_target_ball.xml new file mode 100644 index 0000000..eb2b347 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_target_ball.xml @@ -0,0 +1,10 @@ + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_test_balls.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_test_balls.xml new file mode 100644 index 0000000..29a21e1 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_test_balls.xml @@ -0,0 +1,80 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git 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z%~c`sp-bhuepT{cO3?}D6NL1+i-da4AYE8CdJOD8T)CSn30FC#q>q6L60R5yT91Lm z2d=bHLMP~7X^Kub-!7%UDkRj~C0EKFZ?n=9vVO{vlZtD$>^P=g+T#8L6(n5k+)Z8~ zfiCQi^^~K6gsWe169Wl!VXv*nz~0%_2Rlm5x~E1330Gg@rp%E*m#bGwPG26VAmQq} O+{8cvT{wyw#{UCWK9%MG literal 0 HcmV?d00001 diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml new file mode 100644 index 0000000..9abf102 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml @@ -0,0 +1,19 @@ + + + + + + + + + + + + + + + + + + + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml new file mode 100644 index 0000000..dfbc37a --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml new file mode 100644 index 0000000..f2432bb --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py b/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py new file mode 100644 index 0000000..625a122 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py @@ -0,0 +1,233 @@ +import numpy as np +from gym import spaces +from gym.envs.robotics import robot_env, utils +# import xml.etree.ElementTree as ET +from alr_envs.mujoco.gym_table_tennis.utils.rewards.hierarchical_reward import HierarchicalRewardTableTennis +# import glfw +from alr_envs.mujoco.gym_table_tennis.utils.experiment import ball_initialize +from pathlib import Path +import os + + +class TableTennisEnv(robot_env.RobotEnv): + """Class for Table Tennis environment. + """ + def __init__(self, n_substeps=1, + model_path=None, + initial_qpos=None, + initial_ball_state=None, + config=None, + reward_obj=None + ): + """Initializes a new mujoco based Table Tennis environment. + + Args: + model_path (string): path to the environments XML file + initial_qpos (dict): a dictionary of joint names and values that define the initial + n_actions: Number of joints + n_substeps (int): number of substeps the simulation runs on every call to step + scale (double): limit maximum change in position + initial_ball_state: to reset the ball state + """ + # self.config = config.config + if model_path is None: + path_cws = Path.cwd() + print(path_cws) + current_dir = Path(os.path.split(os.path.realpath(__file__))[0]) + table_tennis_env_xml_path = current_dir / "robotics"/"assets"/"table_tennis"/"table_tennis_env.xml" + model_path = str(table_tennis_env_xml_path) + self.config = config + action_space = self.config['trajectory']['args']['action_space'] + time_step = self.config['mujoco_sim_env']['args']["time_step"] + if initial_qpos is None: + initial_qpos = self.config['robot_config']['args']['initial_qpos'] + + # TODO should read all configuration in config + assert initial_qpos is not None, "Must initialize the initial q position of robot arm" + n_actions = 7 + self.initial_qpos_value = np.array(list(initial_qpos.values())).copy() + # # change time step in .xml file + # tree = ET.parse(model_path) + # root = tree.getroot() + # for option in root.findall('option'): + # option.set("timestep", str(time_step)) + # + # tree.write(model_path) + + super(TableTennisEnv, self).__init__( + model_path=model_path, n_substeps=n_substeps, n_actions=n_actions, + initial_qpos=initial_qpos) + + if action_space: + self.action_space = spaces.Box(low=np.array([-2.6, -2.0, -2.8, -0.9, -4.8, -1.6, -2.2]), + high=np.array([2.6, 2.0, 2.8, 3.1, 1.3, 1.6, 2.2]), + dtype='float64') + else: + self.action_space = spaces.Box(low=np.array([-np.inf] * 7), + high=np.array([-np.inf] * 7), + dtype='float64') + self.scale = None + self.desired_pos = None + self.n_actions = n_actions + self.action = None + self.time_step = time_step + self.paddle_center_pos = self.sim.data.get_site_xpos('wam/paddle_center') + if reward_obj is None: + self.reward_obj = HierarchicalRewardTableTennis() + else: + self.reward_obj = reward_obj + + if initial_ball_state is not None: + self.initial_ball_state = initial_ball_state + else: + self.initial_ball_state = ball_initialize(random=False) + self.target_ball_pos = self.sim.data.get_site_xpos("target_ball") + self.racket_center_pos = self.sim.data.get_site_xpos("wam/paddle_center") + + def close(self): + if self.viewer is not None: + glfw.destroy_window(self.viewer.window) + # self.viewer.window.close() + self.viewer = None + self._viewers = {} + + # GoalEnv methods + # ---------------------------- + def compute_reward(self, achieved_goal, goal, info): + # reset the reward, if action valid + # right_court_contact_obj = ["target_ball", "table_tennis_table_right_side"] + # right_court_contact_detector = self.reward_obj.contact_detection(self, right_court_contact_obj) + # if right_court_contact_detector: + # print("can detect the table ball contact") + self.reward_obj.total_reward = 0 + # Stage 1 Hitting + self.reward_obj.hitting(self) + # if not hitted, return the highest reward + if not self.reward_obj.goal_achievement: + return self.reward_obj.highest_reward + # # Stage 2 Right Table Contact + # self.reward_obj.right_table_contact(self) + # if not self.reward_obj.goal_achievement: + # return self.reward_obj.highest_reward + # # Stage 2 Net Contact + # self.reward_obj.net_contact(self) + # if not self.reward_obj.goal_achievement: + # return self.reward_obj.highest_reward + # Stage 3 Opponent court Contact + # self.reward_obj.landing_on_opponent_court(self) + # if not self.reward_obj.goal_achievement: + # print("self.reward_obj.highest_reward: ", self.reward_obj.highest_reward) + # TODO + self.reward_obj.target_achievement(self) + return self.reward_obj.highest_reward + + def _reset_sim(self): + self.sim.set_state(self.initial_state) + [initial_x, initial_y, initial_z, v_x, v_y, v_z] = self.initial_ball_state + self.sim.data.set_joint_qpos('tar:x', initial_x) + self.sim.data.set_joint_qpos('tar:y', initial_y) + self.sim.data.set_joint_qpos('tar:z', initial_z) + self.energy_corrected = True + self.give_reflection_reward = False + + # velocity is positive direction + self.sim.data.set_joint_qvel('tar:x', v_x) + self.sim.data.set_joint_qvel('tar:y', v_y) + self.sim.data.set_joint_qvel('tar:z', v_z) + + # Apply gravity compensation + if self.sim.data.qfrc_applied[:7] is not self.sim.data.qfrc_bias[:7]: + self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] + self.sim.forward() + return True + + def _env_setup(self, initial_qpos): + for name, value in initial_qpos.items(): + self.sim.data.set_joint_qpos(name, value) + + # Apply gravity compensation + if self.sim.data.qfrc_applied[:7] is not self.sim.data.qfrc_bias[:7]: + self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] + self.sim.forward() + + # Get the target position + self.initial_paddle_center_xpos = self.sim.data.get_site_xpos('wam/paddle_center').copy() + self.initial_paddle_center_vel = None # self.sim.get_site_ + + def _sample_goal(self): + goal = self.initial_paddle_center_xpos[:3] + self.np_random.uniform(-0.2, 0.2, size=3) + return goal.copy() + + def _get_obs(self): + + # positions of racket center + paddle_center_pos = self.sim.data.get_site_xpos('wam/paddle_center') + ball_pos = self.sim.data.get_site_xpos("target_ball") + + dt = self.sim.nsubsteps * self.sim.model.opt.timestep + paddle_center_velp = self.sim.data.get_site_xvelp('wam/paddle_center') * dt + robot_qpos, robot_qvel = utils.robot_get_obs(self.sim) + + wrist_state = robot_qpos[-3:] + wrist_vel = robot_qvel[-3:] * dt # change to a scalar if the gripper is made symmetric + + # achieved_goal = paddle_body_EE_pos + obs = np.concatenate([ + paddle_center_pos, paddle_center_velp, wrist_state, wrist_vel + ]) + + out_dict = { + 'observation': obs.copy(), + 'achieved_goal': paddle_center_pos.copy(), + 'desired_goal': self.goal.copy(), + 'q_pos': self.sim.data.qpos[:].copy(), + "ball_pos": ball_pos.copy(), + # "hitting_flag": self.reward_obj.hitting_flag + } + + return out_dict + + def _step_callback(self): + pass + + def _set_action(self, action): + # Apply gravity compensation + if self.sim.data.qfrc_applied[:7] is not self.sim.data.qfrc_bias[:7]: + self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] + # print("set action process running") + assert action.shape == (self.n_actions,) + self.action = action.copy() # ensure that we don't change the action outside of this scope + pos_ctrl = self.action[:] # limit maximum change in position + pos_ctrl = np.clip(pos_ctrl, self.action_space.low, self.action_space.high) + + # get desired trajectory + self.sim.data.qpos[:7] = pos_ctrl + self.sim.forward() + self.desired_pos = self.sim.data.get_site_xpos('wam/paddle_center').copy() + + self.sim.data.ctrl[:] = pos_ctrl + + def _is_success(self, achieved_goal, desired_goal): + pass + + +if __name__ == '__main__': + render_mode = "human" # "human" or "partial" or "final" + env = TableTennisEnv() + env.reset() + # env.render(mode=render_mode) + + for i in range(200): + # objective.load_result("/tmp/cma") + # test with random actions + ac = 2 * env.action_space.sample() + # ac[0] += np.pi/2 + obs, rew, d, info = env.step(ac) + env.render(mode=render_mode) + + print(rew) + + if d: + break + + env.close() diff --git a/alr_envs/mujoco/gym_table_tennis/utils/__init__.py b/alr_envs/mujoco/gym_table_tennis/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/alr_envs/mujoco/gym_table_tennis/utils/experiment.py b/alr_envs/mujoco/gym_table_tennis/utils/experiment.py new file mode 100644 index 0000000..addd6c5 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/utils/experiment.py @@ -0,0 +1,83 @@ +import numpy as np +from gym.utils import seeding +from alr_envs.mujoco.gym_table_tennis.utils.util import read_yaml, read_json +from pathlib import Path + + +def ball_initialize(random=False, scale=False, context_range=None, scale_value=None): + if random: + if scale: + # if scale_value is None: + scale_value = context_scale_initialize(context_range) + v_x, v_y, v_z = [2.5, 2, 0.5] * scale_value + dx = 1 + dy = 0 + dz = 0.05 + else: + seed = None + np_random, seed = seeding.np_random(seed) + dx = np_random.uniform(-0.1, 0.1) + dy = np_random.uniform(-0.1, 0.1) + dz = np_random.uniform(-0.1, 0.1) + + v_x = np_random.uniform(1.7, 1.8) + v_y = np_random.uniform(0.7, 0.8) + v_z = np_random.uniform(0.1, 0.2) + # print(dx, dy, dz, v_x, v_y, v_z) + # else: + # dx = -0.1 + # dy = 0.05 + # dz = 0.05 + # v_x = 1.5 + # v_y = 0.7 + # v_z = 0.06 + # initial_x = -0.6 + dx + # initial_y = -0.3 + dy + # initial_z = 0.8 + dz + else: + if scale: + v_x, v_y, v_z = [2.5, 2, 0.5] * scale_value + else: + v_x = 2.5 + v_y = 2 + v_z = 0.5 + dx = 1 + dy = 0 + dz = 0.05 + + initial_x = 0 + dx + initial_y = -0.2 + dy + initial_z = 0.3 + dz + # print("initial ball state: ", initial_x, initial_y, initial_z, v_x, v_y, v_z) + initial_ball_state = np.array([initial_x, initial_y, initial_z, v_x, v_y, v_z]) + return initial_ball_state + + +def context_scale_initialize(range): + """ + + Returns: + + """ + low, high = range + scale = np.random.uniform(low, high, 1) + return scale + + +def config_handle_generation(config_file_path): + """Generate config handle for multiprocessing + + Args: + config_file_path: + + Returns: + + """ + cfg_fname = Path(config_file_path) + # .json and .yml file + if cfg_fname.suffix == ".json": + config = read_json(cfg_fname) + elif cfg_fname.suffix == ".yml": + config = read_yaml(cfg_fname) + + return config diff --git a/alr_envs/mujoco/gym_table_tennis/utils/rewards/__init__.py b/alr_envs/mujoco/gym_table_tennis/utils/rewards/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/alr_envs/mujoco/gym_table_tennis/utils/rewards/hierarchical_reward.py b/alr_envs/mujoco/gym_table_tennis/utils/rewards/hierarchical_reward.py new file mode 100644 index 0000000..fe69104 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/utils/rewards/hierarchical_reward.py @@ -0,0 +1,402 @@ +import numpy as np +import logging + + +class HierarchicalRewardTableTennis(object): + """Class for hierarchical reward function for table tennis experiment. + + Return Highest Reward. + Reward = 0 + Step 1: Action Valid. Upper Bound 0 + [-∞, 0] + Reward += -1 * |hit_duration - hit_duration_threshold| * |hit_duration < hit_duration_threshold| * 10 + Step 2: Hitting. Upper Bound 2 + if hitting: + [0, 2] + Reward = 2 * (1 - tanh(|shortest_hitting_dist|)) + if not hitting: + [0, 0.2] + Reward = 2 * (1 - tanh(|shortest_hitting_dist|)) + Step 3: Target Point Achievement. Upper Bound 6 + [0, 4] + if table_contact_detector: + Reward += 1 + Reward += (1 - tanh(|shortest_hitting_dist|)) * 2 + if contact_coordinate[0] < 0: + Reward += 1 + else: + Reward += 0 + elif: + Reward += (1 - tanh(|shortest_hitting_dist|)) + """ + + def __init__(self): + self.reward = None + self.goal_achievement = False + self.total_reward = 0 + self.shortest_hitting_dist = 1000 + self.highest_reward = -1000 + self.lowest_corner_dist = 100 + self.right_court_contact_detector = False + self.table_contact_detector = False + self.floor_contact_detector = False + self.radius = 0.025 + self.min_ball_x_pos = 100 + self.hit_contact_detector = False + self.net_contact_detector = False + self.ratio = 1 + self.lowest_z = 100 + self.target_flag = False + self.dist_target_virtual = 100 + self.ball_z_pos_lowest = 100 + self.hitting_flag = False + self.hitting_time_point = None + self.ctxt_dim = None + self.context_range_bounds = None + # self.ctxt_out_of_range_punishment = None + # self.ctxt_in_side_of_range_punishment = None + # + # def check_where_invalid(self, ctxt, context_range_bounds, set_to_valid_region=False): + # idx_max = [] + # idx_min = [] + # for dim in range(self.ctxt_dim): + # min_dim = context_range_bounds[0][dim] + # max_dim = context_range_bounds[1][dim] + # idx_max_c = np.where(ctxt[:, dim] > max_dim)[0] + # idx_min_c = np.where(ctxt[:, dim] < min_dim)[0] + # if set_to_valid_region: + # if idx_max_c.shape[0] != 0: + # ctxt[idx_max_c, dim] = max_dim + # if idx_min_c.shape[0] != 0: + # ctxt[idx_min_c, dim] = min_dim + # idx_max.append(idx_max_c) + # idx_min.append(idx_min_c) + # return idx_max, idx_min, ctxt + + def check_valid(self, scale, context_range_bounds): + + min_dim = context_range_bounds[0][0] + max_dim = context_range_bounds[1][0] + valid = (scale < max_dim) and (scale > min_dim) + return valid + + @classmethod + def goal_distance(cls, goal_a, goal_b): + assert goal_a.shape == goal_b.shape + return np.linalg.norm(goal_a - goal_b, axis=-1) + + def refresh_highest_reward(self): + if self.total_reward >= self.highest_reward: + self.highest_reward = self.total_reward + + def duration_valid(self): + pass + + def huge_value_unstable(self): + self.total_reward += -10 + self.highest_reward = -1 + + def context_valid(self, context): + valid = self.check_valid(context.copy(), context_range_bounds=self.context_range_bounds) + # when using dirac punishments + if valid: + self.total_reward += 1 # If Action Valid and Context Valid, total_reward = 0 + else: + self.total_reward += 0 + self.refresh_highest_reward() + + + + # If in the ctxt, add 1, otherwise, 0 + + def action_valid(self, durations=None): + """Ensure the execution of the robot movement with parameters which are in a valid domain. + + Time should always be positive, + the joint position of the robot should be a subset of [−π, π]. + if all parameters are valid, the robot gets a zero score, + otherwise it gets a negative score proportional to how much it is beyond the valid parameter domain. + + Returns: + rewards: if valid, reward is equal to 0. + if not valid, reward is negative and proportional to the distance beyond the valid parameter domain + """ + assert durations.shape[0] == 2, "durations type should be np.array and the shape should be 2" + # pre_duration = durations[0] + hit_duration = durations[1] + # pre_duration_thres = 0.01 + hit_duration_thres = 1 + # self.goal_achievement = np.all( + # [(pre_duration > pre_duration_thres), (hit_duration > hit_duration_thres), (0.3 < pre_duration < 0.6)]) + self.goal_achievement = (hit_duration > hit_duration_thres) + if self.goal_achievement: + self.total_reward = -1 + self.goal_achievement = True + else: + # self.total_reward += -1 * ((np.abs(pre_duration - pre_duration_thres) * int( + # pre_duration < pre_duration_thres) + np.abs(hit_duration - hit_duration_thres) * int( + # hit_duration < hit_duration_thres)) * 10) + self.total_reward = -1 * ((np.abs(hit_duration - hit_duration_thres) * int( + hit_duration < hit_duration_thres)) * 10) + self.total_reward += -1 + self.goal_achievement = False + self.refresh_highest_reward() + + def motion_penalty(self, action, high_motion_penalty): + """Protects the robot from high acceleration and dangerous movement. + """ + if not high_motion_penalty: + reward_ctrl = - 0.05 * np.square(action).sum() + else: + reward_ctrl = - 0.075 * np.square(action).sum() + self.total_reward += reward_ctrl + self.refresh_highest_reward() + self.goal_achievement = True + + def hitting(self, env): # , target_ball_pos, racket_center_pos, hit_contact_detector=False + """Hitting reward calculation + + If racket successfully hit the ball, the reward +1 + Otherwise calculate the distance between the center of racket and the center of ball, + reward = tanh(r/dist) if dist<1 reward almost 2 , if dist >= 1 reward is between [0, 0.2] + + + Args: + env: + + Returns: + + """ + + hit_contact_obj = ["target_ball", "bat"] + target_ball_pos = env.target_ball_pos + racket_center_pos = env.racket_center_pos + # hit contact detection + # Record the hitting history + self.hitting_flag = False + if not self.hit_contact_detector: + self.hit_contact_detector = self.contact_detection(env, hit_contact_obj) + if self.hit_contact_detector: + print("First time detect hitting") + self.hitting_flag = True + if self.hit_contact_detector: + + # TODO + dist = self.goal_distance(target_ball_pos, racket_center_pos) + if dist < 0: + dist = 0 + # print("goal distance is:", dist) + if dist <= self.shortest_hitting_dist: + self.shortest_hitting_dist = dist + # print("shortest_hitting_dist is:", self.shortest_hitting_dist) + # Keep the shortest hitting distance. + dist_reward = 2 * (1 - np.tanh(np.abs(self.shortest_hitting_dist))) + + # TODO sparse + # dist_reward = 2 + + self.total_reward += dist_reward + self.goal_achievement = True + + # if self.hitting_time_point is not None and self.hitting_time_point > 600: + # self.total_reward += 1 + + else: + dist = self.goal_distance(target_ball_pos, racket_center_pos) + if dist <= self.shortest_hitting_dist: + self.shortest_hitting_dist = dist + dist_reward = 1 - np.tanh(self.shortest_hitting_dist) + reward = 0.2 * dist_reward # because it does not hit the ball, so multiply 0.2 + self.total_reward += reward + self.goal_achievement = False + + self.refresh_highest_reward() + + @classmethod + def relu(cls, x): + return np.maximum(0, x) + + # def right_table_contact(self, env): + # right_court_contact_obj = ["target_ball", "table_tennis_table_right_side"] + # if env.target_ball_pos[0] >= 0 and env.target_ball_pos[2] >= 0.7: + # # update right court contact detection + # if not self.right_court_contact_detector: + # self.right_court_contact_detector = self.contact_detection(env, right_court_contact_obj) + # if self.right_court_contact_detector: + # self.contact_x_pos = env.target_ball_pos[0] + # if self.right_court_contact_detector: + # self.total_reward += 1 - norm(0.685, 1).pdf(self.contact_x_pos) # x axis middle of right table + # self.goal_achievement = False + # else: + # self.total_reward += 1 + # self.goal_achievement = True + # # else: + # # self.total_reward += 0 + # # self.goal_achievement = False + # self.refresh_highest_reward() + + # def net_contact(self, env): + # net_contact_obj = ["target_ball", "table_tennis_net"] + # # net_contact_detector = self.contact_detection(env, net_contact_obj) + # # ball_x_pos = env.target_ball_pos[0] + # # if self.min_ball_x_pos >= ball_x_pos: + # # self.min_ball_x_pos = ball_x_pos + # # table_left_edge_x_pos = -1.37 + # # if np.abs(ball_x_pos) <= 0.01: # x threshold of net + # # if self.lowest_z >= env.target_ball_pos[2]: + # # self.lowest_z = env.target_ball_pos[2] + # # # construct a gaussian distribution of z + # # z_reward = 4 - norm(0, 0.1).pdf(self.lowest_z - 0.07625) # maximum 4 + # # self.total_reward += z_reward + # # self.total_reward += 2 - np.minimum(1, self.relu(np.abs(self.min_ball_x_pos))) + # if not self.net_contact_detector: + # self.net_contact_detector = self.contact_detection(env, net_contact_obj) + # if self.net_contact_detector: + # self.total_reward += 0 # very high cost + # self.goal_achievement = False + # else: + # self.total_reward += 1 + # self.goal_achievement = True + # self.refresh_highest_reward() + + # def landing_on_opponent_court(self, env): + # # Very sparse reward + # # don't contact the right side court + # # right_court_contact_obj = ["target_ball", "table_tennis_table_right_side"] + # # right_court_contact_detector = self.contact_detection(env, right_court_contact_obj) + # left_court_contact_obj = ["target_ball", "table_tennis_table_left_side"] + # # left_court_contact_detector = self.contact_detection(env, left_court_contact_obj) + # # record the contact history + # # if not self.right_court_contact_detector: + # # self.right_court_contact_detector = self.contact_detection(env, right_court_contact_obj) + # if not self.table_contact_detector: + # self.table_contact_detector = self.contact_detection(env, left_court_contact_obj) + # + # dist_left_up_corner = self.goal_distance(env.target_ball_pos, env.sim.data.get_site_xpos("left_up_corner")) + # dist_middle_up_corner = self.goal_distance(env.target_ball_pos, env.sim.data.get_site_xpos("middle_up_corner")) + # dist_left_down_corner = self.goal_distance(env.target_ball_pos, env.sim.data.get_site_xpos("left_down_corner")) + # dist_middle_down_corner = self.goal_distance(env.target_ball_pos, + # env.sim.data.get_site_xpos("middle_down_corner")) + # dist_array = np.array( + # [dist_left_up_corner, dist_middle_up_corner, dist_left_down_corner, dist_middle_down_corner]) + # dist_corner = np.amin(dist_array) + # if self.lowest_corner_dist >= dist_corner: + # self.lowest_corner_dist = dist_corner + # + # right_contact_cost = 1 + # left_contact_reward = 2 + # dist_left_table_reward = (2 - np.tanh(self.lowest_corner_dist)) + # # TODO Try multi dimensional gaussian distribution + # # contact only the left side court + # if self.right_court_contact_detector: + # self.total_reward += 0 + # self.goal_achievement = False + # if self.table_contact_detector: + # self.total_reward += left_contact_reward + # self.goal_achievement = False + # else: + # self.total_reward += dist_left_table_reward + # self.goal_achievement = False + # else: + # self.total_reward += right_contact_cost + # if self.table_contact_detector: + # self.total_reward += left_contact_reward + # self.goal_achievement = True + # else: + # self.total_reward += dist_left_table_reward + # self.goal_achievement = False + # self.refresh_highest_reward() + # # if self.left_court_contact_detector and not self.right_court_contact_detector: + # # self.total_reward += self.ratio * left_contact_reward + # # print("only left court reward return!!!!!!!!!") + # # print("contact only left court!!!!!!") + # # self.goal_achievement = True + # # # no contact with table + # # elif not self.right_court_contact_detector and not self.left_court_contact_detector: + # # self.total_reward += 0 + self.ratio * dist_left_table_reward + # # self.goal_achievement = False + # # # contact both side + # # elif self.right_court_contact_detector and self.left_court_contact_detector: + # # self.total_reward += self.ratio * (left_contact_reward - right_contact_cost) # cost of contact of right court + # # self.goal_achievement = False + # # # contact only the right side court + # # elif self.right_court_contact_detector and not self.left_court_contact_detector: + # # self.total_reward += 0 + self.ratio * ( + # # dist_left_table_reward - right_contact_cost) # cost of contact of right court + # # self.goal_achievement = False + + def target_achievement(self, env): + target_coordinate = np.array([-0.5, -0.5]) + # net_contact_obj = ["target_ball", "table_tennis_net"] + table_contact_obj = ["target_ball", "table_tennis_table"] + floor_contact_obj = ["target_ball", "floor"] + + if 0.78 < env.target_ball_pos[2] < 0.8: + dist_target_virtual = np.linalg.norm(env.target_ball_pos[:2] - target_coordinate) + if self.dist_target_virtual > dist_target_virtual: + self.dist_target_virtual = dist_target_virtual + if -0.07 < env.target_ball_pos[0] < 0.07 and env.sim.data.get_joint_qvel('tar:x') < 0: + if self.ball_z_pos_lowest > env.target_ball_pos[2]: + self.ball_z_pos_lowest = env.target_ball_pos[2].copy() + # if not self.net_contact_detector: + # self.net_contact_detector = self.contact_detection(env, net_contact_obj) + if not self.table_contact_detector: + self.table_contact_detector = self.contact_detection(env, table_contact_obj) + if not self.floor_contact_detector: + self.floor_contact_detector = self.contact_detection(env, floor_contact_obj) + if not self.target_flag: + # Table Contact Reward. + if self.table_contact_detector: + self.total_reward += 1 + # only update when the first contact because of the flag + contact_coordinate = env.target_ball_pos[:2].copy() + print("contact table ball coordinate: ", env.target_ball_pos) + logging.info("contact table ball coordinate: {}".format(env.target_ball_pos)) + dist_target = np.linalg.norm(contact_coordinate - target_coordinate) + self.total_reward += (1 - np.tanh(dist_target)) * 2 + self.target_flag = True + # Net Contact Reward. Precondition: Table Contact exits. + if contact_coordinate[0] < 0: + print("left table contact") + logging.info("~~~~~~~~~~~~~~~left table contact~~~~~~~~~~~~~~~") + self.total_reward += 1 + # TODO Z coordinate reward + # self.total_reward += np.maximum(np.tanh(self.ball_z_pos_lowest), 0) + self.goal_achievement = True + else: + print("right table contact") + logging.info("~~~~~~~~~~~~~~~right table contact~~~~~~~~~~~~~~~") + self.total_reward += 0 + self.goal_achievement = False + # if self.net_contact_detector: + # self.total_reward += 0 + # self.goal_achievement = False + # else: + # self.total_reward += 1 + # self.goal_achievement = False + # Floor Contact Reward. Precondition: Table Contact exits. + elif self.floor_contact_detector: + self.total_reward += (1 - np.tanh(self.dist_target_virtual)) + self.target_flag = True + self.goal_achievement = False + # No Contact of Floor or Table, flying + else: + pass + # else: + # print("Flag is True already") + self.refresh_highest_reward() + + def distance_to_target(self): + pass + + @classmethod + def contact_detection(cls, env, goal_contact): + for i in range(env.sim.data.ncon): + contact = env.sim.data.contact[i] + achieved_geom1_name = env.sim.model.geom_id2name(contact.geom1) + achieved_geom2_name = env.sim.model.geom_id2name(contact.geom2) + if np.all([(achieved_geom1_name in goal_contact), (achieved_geom2_name in goal_contact)]): + print("contact of " + achieved_geom1_name + " " + achieved_geom2_name) + return True + else: + return False diff --git a/alr_envs/mujoco/gym_table_tennis/utils/rewards/rewards.py b/alr_envs/mujoco/gym_table_tennis/utils/rewards/rewards.py new file mode 100644 index 0000000..6e6aa32 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/utils/rewards/rewards.py @@ -0,0 +1,136 @@ +# Copyright 2017 The dm_control Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ + +# """Soft indicator function evaluating whether a number is within bounds.""" +# +# from __future__ import absolute_import +# from __future__ import division +# from __future__ import print_function + +# Internal dependencies. +import numpy as np + +# The value returned by tolerance() at `margin` distance from `bounds` interval. +_DEFAULT_VALUE_AT_MARGIN = 0.1 + + +def _sigmoids(x, value_at_1, sigmoid): + """Returns 1 when `x` == 0, between 0 and 1 otherwise. + + Args: + x: A scalar or numpy array. + value_at_1: A float between 0 and 1 specifying the output when `x` == 1. + sigmoid: String, choice of sigmoid type. + + Returns: + A numpy array with values between 0.0 and 1.0. + + Raises: + ValueError: If not 0 < `value_at_1` < 1, except for `linear`, `cosine` and + `quadratic` sigmoids which allow `value_at_1` == 0. + ValueError: If `sigmoid` is of an unknown type. + """ + if sigmoid in ('cosine', 'linear', 'quadratic'): + if not 0 <= value_at_1 < 1: + raise ValueError('`value_at_1` must be nonnegative and smaller than 1, ' + 'got {}.'.format(value_at_1)) + else: + if not 0 < value_at_1 < 1: + raise ValueError('`value_at_1` must be strictly between 0 and 1, ' + 'got {}.'.format(value_at_1)) + + if sigmoid == 'gaussian': + scale = np.sqrt(-2 * np.log(value_at_1)) + return np.exp(-0.5 * (x*scale)**2) + + elif sigmoid == 'hyperbolic': + scale = np.arccosh(1/value_at_1) + return 1 / np.cosh(x*scale) + + elif sigmoid == 'long_tail': + scale = np.sqrt(1/value_at_1 - 1) + return 1 / ((x*scale)**2 + 1) + + elif sigmoid == 'cosine': + scale = np.arccos(2*value_at_1 - 1) / np.pi + scaled_x = x*scale + return np.where(abs(scaled_x) < 1, (1 + np.cos(np.pi*scaled_x))/2, 0.0) + + elif sigmoid == 'linear': + scale = 1-value_at_1 + scaled_x = x*scale + return np.where(abs(scaled_x) < 1, 1 - scaled_x, 0.0) + + elif sigmoid == 'quadratic': + scale = np.sqrt(1-value_at_1) + scaled_x = x*scale + return np.where(abs(scaled_x) < 1, 1 - scaled_x**2, 0.0) + + elif sigmoid == 'tanh_squared': + scale = np.arctanh(np.sqrt(1-value_at_1)) + return 1 - np.tanh(x*scale)**2 + + else: + raise ValueError('Unknown sigmoid type {!r}.'.format(sigmoid)) + + +def tolerance(x, bounds=(0.0, 0.0), margin=0.0, sigmoid='gaussian', + value_at_margin=_DEFAULT_VALUE_AT_MARGIN): + """Returns 1 when `x` falls inside the bounds, between 0 and 1 otherwise. + + Args: + x: A scalar or numpy array. + bounds: A tuple of floats specifying inclusive `(lower, upper)` bounds for + the target interval. These can be infinite if the interval is unbounded + at one or both ends, or they can be equal to one another if the target + value is exact. + margin: Float. Parameter that controls how steeply the output decreases as + `x` moves out-of-bounds. + * If `margin == 0` then the output will be 0 for all values of `x` + outside of `bounds`. + * If `margin > 0` then the output will decrease sigmoidally with + increasing distance from the nearest bound. + sigmoid: String, choice of sigmoid type. Valid values are: 'gaussian', + 'linear', 'hyperbolic', 'long_tail', 'cosine', 'tanh_squared'. + value_at_margin: A float between 0 and 1 specifying the output value when + the distance from `x` to the nearest bound is equal to `margin`. Ignored + if `margin == 0`. + + Returns: + A float or numpy array with values between 0.0 and 1.0. + + Raises: + ValueError: If `bounds[0] > bounds[1]`. + ValueError: If `margin` is negative. + """ + lower, upper = bounds + if lower > upper: + raise ValueError('Lower bound must be <= upper bound.') + if margin < 0: + raise ValueError('`margin` must be non-negative.') + + in_bounds = np.logical_and(lower <= x, x <= upper) + if margin == 0: + value = np.where(in_bounds, 1.0, 0.0) + else: + d = np.where(x < lower, lower - x, x - upper) / margin + value = np.where(in_bounds, 1.0, _sigmoids(d, value_at_margin, sigmoid)) + + return float(value) if np.isscalar(x) else value + + + + + diff --git a/alr_envs/mujoco/gym_table_tennis/utils/util.py b/alr_envs/mujoco/gym_table_tennis/utils/util.py new file mode 100644 index 0000000..716b3c6 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/utils/util.py @@ -0,0 +1,49 @@ +import json +import yaml +import xml.etree.ElementTree as ET +from collections import OrderedDict +from pathlib import Path + + +def read_json(fname): + fname = Path(fname) + with fname.open('rt') as handle: + return json.load(handle, object_hook=OrderedDict) + + +def write_json(content, fname): + fname = Path(fname) + with fname.open('wt') as handle: + json.dump(content, handle, indent=4, sort_keys=False) + + +def read_yaml(fname): + fname = Path(fname) + with fname.open('rt') as handle: + return yaml.load(handle, Loader=yaml.FullLoader) + + +def write_yaml(content, fname): + fname = Path(fname) + with fname.open('wt') as handle: + yaml.dump(content, handle) + + +def config_save(dir_path, config): + dir_path = Path(dir_path) + config_path_json = dir_path / "config.json" + config_path_yaml = dir_path / "config.yml" + # .json and .yml file,save 2 version of configuration. + write_json(config, config_path_json) + write_yaml(config, config_path_yaml) + + +def change_kp_in_xml(kp_list, + model_path="/home/zhou/slow/table_tennis_rl/simulation/gymTableTennis/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml"): + tree = ET.parse(model_path) + root = tree.getroot() + # for actuator in root.find("actuator"): + for position, kp in zip(root.iter('position'), kp_list): + position.set("kp", str(kp)) + tree.write(model_path) + diff --git a/example.py b/example.py index cc96611..fd11c73 100644 --- a/example.py +++ b/example.py @@ -4,8 +4,8 @@ import gym if __name__ == '__main__': - # env = gym.make('alr_envs:ALRReacher-v0') - env = gym.make('alr_envs:SimpleReacher-v0') + env = gym.make('alr_envs:ALRReacher-v0') + # env = gym.make('alr_envs:SimpleReacher-v0') # env = gym.make('alr_envs:ALRReacher7-v0') state = env.reset() From 07195fa2dc4ae4b87fa65ef0027997bd6db31b08 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Fri, 5 Feb 2021 17:10:03 +0100 Subject: [PATCH 06/43] lots of new stuff... --- alr_envs/mujoco/__init__.py | 2 +- alr_envs/mujoco/ball_in_a_cup/__init__.py | 0 .../assets/ball-in-a-cup_base.xml | 366 ++++++++++++++++++ .../assets}/meshes/base_link_convex.stl | Bin .../assets}/meshes/base_link_fine.stl | Bin .../ball_in_a_cup/assets/meshes/cup.stl | Bin 0 -> 187084 bytes .../assets/meshes/cup_split1.stl | Bin 0 -> 2484 bytes .../assets/meshes/cup_split10.stl | Bin 0 -> 1684 bytes .../assets/meshes/cup_split11.stl | Bin 0 -> 1884 bytes .../assets/meshes/cup_split12.stl | Bin 0 -> 2084 bytes .../assets/meshes/cup_split13.stl | Bin 0 -> 1684 bytes .../assets/meshes/cup_split14.stl | Bin 0 -> 1484 bytes .../assets/meshes/cup_split15.stl | Bin 0 -> 1584 bytes .../assets/meshes/cup_split16.stl | Bin 0 -> 1684 bytes .../assets/meshes/cup_split17.stl | Bin 0 -> 1484 bytes .../assets/meshes/cup_split18.stl | Bin 0 -> 1784 bytes .../assets/meshes/cup_split2.stl | Bin 0 -> 1484 bytes .../assets/meshes/cup_split3.stl | Bin 0 -> 1884 bytes .../assets/meshes/cup_split4.stl | Bin 0 -> 1584 bytes 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.../assets}/meshes/wrist_palm_link_convex.stl | Bin .../assets}/meshes/wrist_palm_link_fine.stl | Bin ...ist_pitch_link_convex_decomposition_p1.stl | Bin ...ist_pitch_link_convex_decomposition_p2.stl | Bin ...ist_pitch_link_convex_decomposition_p3.stl | Bin .../assets}/meshes/wrist_pitch_link_fine.stl | Bin ...wrist_yaw_link_convex_decomposition_p1.stl | Bin ...wrist_yaw_link_convex_decomposition_p2.stl | Bin .../assets}/meshes/wrist_yaw_link_fine.stl | Bin .../mujoco/ball_in_a_cup/ball_in_a_cup.py | 117 ++++++ .../ball_in_a_cup/ball_in_a_cup_reward.py | 127 ++++++ .../ball_in_a_cup/ball_in_a_cup_simple.py | 210 ++++++++++ .../gym_table_tennis/envs/MUJOCO_LOG.TXT | 3 + .../include_7_motor_actuator.xml | 0 .../include_barrett_wam_7dof_left.xml | 0 .../include_barrett_wam_7dof_right.xml | 0 .../table_tennis => assets}/include_table.xml | 0 .../include_target_ball.xml | 0 .../include_test_balls.xml | 0 .../envs/assets/meshes/base_link_convex.stl | Bin 0 -> 16584 bytes .../envs/assets/meshes/base_link_fine.stl | Bin 0 -> 720584 bytes .../meshes/bhand_finger_dist_link_convex.stl | Bin .../meshes/bhand_finger_dist_link_fine.stl | Bin .../meshes/bhand_finger_med_link_convex.stl | Bin .../meshes/bhand_finger_med_link_fine.stl | Bin ...nger_prox_link_convex_decomposition_p1.stl | Bin ...nger_prox_link_convex_decomposition_p2.stl | Bin ...nger_prox_link_convex_decomposition_p3.stl | Bin .../meshes/bhand_finger_prox_link_fine.stl | Bin .../meshes/bhand_palm_fine.stl | Bin ...hand_palm_link_convex_decomposition_p1.stl | Bin ...hand_palm_link_convex_decomposition_p2.stl | Bin ...hand_palm_link_convex_decomposition_p3.stl | Bin ...hand_palm_link_convex_decomposition_p4.stl | Bin .../envs/assets/meshes/elbow_link_convex.stl | Bin 0 -> 16484 bytes .../envs/assets/meshes/elbow_link_fine.stl | Bin 0 -> 141384 bytes .../forearm_link_convex_decomposition_p1.stl | Bin 0 -> 33484 bytes .../forearm_link_convex_decomposition_p2.stl | Bin 0 -> 205484 bytes 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alr_envs/mujoco/ball_in_a_cup/assets/ball-in-a-cup_base.xml rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/base_link_convex.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/base_link_fine.stl (100%) mode change 100644 => 100755 create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split1.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split10.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split11.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split12.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split13.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split14.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split15.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split16.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split17.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split18.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split2.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split3.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split4.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split5.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split6.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split7.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split8.stl create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/meshes/cup_split9.stl rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/elbow_link_convex.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/elbow_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/forearm_link_convex_decomposition_p1.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/forearm_link_convex_decomposition_p2.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/forearm_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_link_convex_decomposition_p1.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_link_convex_decomposition_p2.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_link_convex_decomposition_p3.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_pitch_link_convex.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/shoulder_pitch_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/upper_arm_link_convex_decomposition_p1.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/upper_arm_link_convex_decomposition_p2.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/upper_arm_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_palm_link_convex.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_palm_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_pitch_link_convex_decomposition_p1.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_pitch_link_convex_decomposition_p2.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_pitch_link_convex_decomposition_p3.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_pitch_link_fine.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_yaw_link_convex_decomposition_p1.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_yaw_link_convex_decomposition_p2.stl (100%) mode change 100644 => 100755 rename alr_envs/mujoco/{gym_table_tennis/envs/robotics/assets/table_tennis => ball_in_a_cup/assets}/meshes/wrist_yaw_link_fine.stl (100%) mode change 100644 => 100755 create mode 100644 alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py create mode 100644 alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py create mode 100644 alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/MUJOCO_LOG.TXT rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_7_motor_actuator.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_barrett_wam_7dof_left.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_barrett_wam_7dof_right.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_table.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_target_ball.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/include_test_balls.xml (100%) create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/base_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/base_link_fine.stl rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_dist_link_convex.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_dist_link_fine.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_med_link_convex.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_med_link_fine.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_prox_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_prox_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_prox_link_convex_decomposition_p3.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_finger_prox_link_fine.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_palm_fine.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_palm_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_palm_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_palm_link_convex_decomposition_p3.stl (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/meshes/bhand_palm_link_convex_decomposition_p4.stl (100%) create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/elbow_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/elbow_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/forearm_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/forearm_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/forearm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_pitch_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/shoulder_pitch_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/upper_arm_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/upper_arm_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/upper_arm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_palm_link_convex.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_palm_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_pitch_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_pitch_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_pitch_link_convex_decomposition_p3.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_pitch_link_fine.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_yaw_link_convex_decomposition_p1.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_yaw_link_convex_decomposition_p2.stl create mode 100644 alr_envs/mujoco/gym_table_tennis/envs/assets/meshes/wrist_yaw_link_fine.stl rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/right_arm_actuator.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/shared.xml (100%) rename alr_envs/mujoco/gym_table_tennis/envs/{robotics/assets/table_tennis => assets}/table_tennis_env.xml (100%) create mode 100644 alr_envs/mujoco/reacher/__init__.py rename alr_envs/mujoco/{ => reacher}/alr_reacher.py (80%) rename alr_envs/mujoco/{ => reacher}/assets/reacher_5links.xml (100%) rename alr_envs/mujoco/{ => reacher}/assets/reacher_7links.xml (100%) create mode 100644 alr_envs/utils/detpmp_env_wrapper.py create mode 100644 alr_envs/utils/policies.py create mode 100644 dmp_pd_control_example.py diff --git a/alr_envs/mujoco/__init__.py b/alr_envs/mujoco/__init__.py index 77588f7..e0ecd85 100644 --- a/alr_envs/mujoco/__init__.py +++ b/alr_envs/mujoco/__init__.py @@ -1 +1 @@ -from alr_envs.mujoco.alr_reacher import ALRReacherEnv \ No newline at end of file +from alr_envs.mujoco.reacher.alr_reacher import ALRReacherEnv \ No newline at end of file diff --git a/alr_envs/mujoco/ball_in_a_cup/__init__.py b/alr_envs/mujoco/ball_in_a_cup/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/alr_envs/mujoco/ball_in_a_cup/assets/ball-in-a-cup_base.xml b/alr_envs/mujoco/ball_in_a_cup/assets/ball-in-a-cup_base.xml new file mode 100644 index 0000000..b534205 --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/assets/ball-in-a-cup_base.xml @@ -0,0 +1,366 @@ + + + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_convex.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/base_link_convex.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_convex.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/base_link_convex.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/base_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/base_link_fine.stl rename to 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alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_convex_decomposition_p2.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/forearm_link_convex_decomposition_p2.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/forearm_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/forearm_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/forearm_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p1.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p1.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p1.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p1.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p2.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p2.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p2.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p2.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p3.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p3.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_convex_decomposition_p3.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_convex_decomposition_p3.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_convex.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_pitch_link_convex.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_convex.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_pitch_link_convex.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_pitch_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/shoulder_pitch_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/shoulder_pitch_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p1.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_convex_decomposition_p1.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p1.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_convex_decomposition_p1.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p2.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_convex_decomposition_p2.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_convex_decomposition_p2.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_convex_decomposition_p2.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/upper_arm_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/upper_arm_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_convex.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_palm_link_convex.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_convex.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_palm_link_convex.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_palm_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_palm_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_palm_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p1.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p1.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p1.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p1.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p2.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p2.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p2.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p2.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p3.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p3.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_convex_decomposition_p3.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_convex_decomposition_p3.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_pitch_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_pitch_link_fine.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p1.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_convex_decomposition_p1.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p1.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_convex_decomposition_p1.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p2.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_convex_decomposition_p2.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_convex_decomposition_p2.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_convex_decomposition_p2.stl diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_fine.stl b/alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_fine.stl old mode 100644 new mode 100755 similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/meshes/wrist_yaw_link_fine.stl rename to alr_envs/mujoco/ball_in_a_cup/assets/meshes/wrist_yaw_link_fine.stl diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py new file mode 100644 index 0000000..093e7f1 --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py @@ -0,0 +1,117 @@ +from gym.envs.mujoco import mujoco_env +from gym import utils +import os +import numpy as np +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward +import mujoco_py + + +class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): + def __init__(self, ): + self._steps = 0 + + self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", + "ball-in-a-cup_base" + ".xml") + + self.sim_time = 8 # seconds + self.sim_steps = int(self.sim_time / (0.0005 * 4)) # circular dependency.. sim.dt <-> mujocoenv init <-> reward fct + self.reward_function = BallInACupReward(self.sim_steps) + + self.start_pos = np.array([0.0, 0.58760536, 0.0, 1.36004913, 0.0, -0.32072943, -1.57]) + self._q_pos = [] + + utils.EzPickle.__init__(self) + mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), + frame_skip=4) + + def reset_model(self): + start_pos = self.init_qpos.copy() + start_pos[0:7] = self.start_pos + start_vel = np.zeros_like(start_pos) + self.set_state(start_pos, start_vel) + self._steps = 0 + self.reward_function.reset() + self._q_pos = [] + + def do_simulation(self, ctrl, n_frames): + self.sim.data.ctrl[:] = ctrl + for _ in range(n_frames): + try: + self.sim.step() + except mujoco_py.builder.MujocoException as e: + # print("Error in simulation: " + str(e)) + # error = True + # Copy the current torque as if it would have been applied until the end of the trajectory + # for i in range(k + 1, sim_time): + # torques.append(trq) + return True + + return False + + def step(self, a): + # Apply gravity compensation + if not np.all(self.sim.data.qfrc_applied[:7] == self.sim.data.qfrc_bias[:7]): + self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] + + reward_dist = 0.0 + angular_vel = 0.0 + # if self._steps >= self.steps_before_reward: + # vec = self.get_body_com("fingertip") - self.get_body_com("target") + # reward_dist -= self.reward_weight * np.linalg.norm(vec) + # angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links]) + reward_ctrl = - np.square(a).sum() + # reward_balance = - self.balance_weight * np.abs( + # angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad")) + # + # reward = reward_dist + reward_ctrl + angular_vel + reward_balance + # self.do_simulation(a, self.frame_skip) + + crash = self.do_simulation(a, self.frame_skip) + + self._q_pos.append(self.sim.data.qpos[0:7].ravel().copy()) + + ob = self._get_obs() + + if not crash: + reward, success, collision = self.reward_function.compute_reward(a, self.sim, self._steps) + done = success or self._steps == self.sim_steps - 1 or collision + self._steps += 1 + else: + reward = -1000 + done = True + return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl, + velocity=angular_vel, # reward_balance=reward_balance, + # end_effector=self.get_body_com("fingertip").copy(), + goal=self.goal if hasattr(self, "goal") else None, + traj=self._q_pos) + + def _get_obs(self): + theta = self.sim.data.qpos.flat[:7] + return np.concatenate([ + np.cos(theta), + np.sin(theta), + # self.get_body_com("target"), # only return target to make problem harder + [self._steps], + ]) + + + +if __name__ == "__main__": + env = ALRBallInACupEnv() + env.reset() + for i in range(2000): + # objective.load_result("/tmp/cma") + # test with random actions + # ac = 0.0 * env.action_space.sample() + ac = env.start_pos + # ac[0] += np.pi/2 + obs, rew, d, info = env.step(ac) + env.render() + + print(rew) + + if d: + break + + env.close() + diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py new file mode 100644 index 0000000..69932db --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -0,0 +1,127 @@ +import numpy as np + + +class BallInACupReward: + def __init__(self, sim_time): + self.sim_time = sim_time + + self.collision_objects = ["cup_geom1", "cup_geom2", "wrist_palm_link_convex_geom", + "wrist_pitch_link_convex_decomposition_p1_geom", + "wrist_pitch_link_convex_decomposition_p2_geom", + "wrist_pitch_link_convex_decomposition_p3_geom", + "wrist_yaw_link_convex_decomposition_p1_geom", + "wrist_yaw_link_convex_decomposition_p2_geom", + "forearm_link_convex_decomposition_p1_geom", + "forearm_link_convex_decomposition_p2_geom"] + + self.ctxt_id = None + self.ball_id = None + self.ball_collision_id = None + self.goal_id = None + self.goal_final_id = None + self.collision_ids = None + + self.ball_traj = None + self.dists = None + self.dists_final = None + self.costs = None + + self.reset() + + def reset(self): + self.ball_traj = np.zeros(shape=(self.sim_time, 3)) + self.dists = [] + self.dists_final = [] + self.costs = [] + + def compute_reward(self, action, sim, step): + self.ctxt_id = sim.model._site_name2id['context_point'] + self.ball_id = sim.model._body_name2id["ball"] + self.ball_collision_id = sim.model._geom_name2id["ball_geom"] + self.goal_id = sim.model._site_name2id["cup_goal"] + self.goal_final_id = sim.model._site_name2id["cup_goal_final"] + self.collision_ids = [sim.model._geom_name2id[name] for name in self.collision_objects] + + ball_in_cup = self.check_ball_in_cup(sim, self.ball_collision_id) + + # Compute the current distance from the ball to the inner part of the cup + goal_pos = sim.data.site_xpos[self.goal_id] + ball_pos = sim.data.body_xpos[self.ball_id] + goal_final_pos = sim.data.site_xpos[self.goal_final_id] + self.dists.append(np.linalg.norm(goal_pos - ball_pos)) + self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) + # dists_ctxt.append(np.linalg.norm(ball_pos - ctxt)) + self.ball_traj[step, :] = ball_pos + + if self.check_collision(sim): + return -1000, False, True + + # self._get_cost(ball_pos, goal_pos, goal_final_pos, action, + # sim.data.get_site_xpos('context_point').copy(), step) + + # min_dist = np.min(self.dists) + # dist_final = self.dists_final[-1] + action_cost = np.sum(np.square(action)) + + # cost = self.get_stage_wise_cost(ball_in_cup, min_dist, self.dists_final[-1]) # , self.dists_ctxt[-1]) + if step == self.sim_time - 1: + min_dist = np.min(self.dists) + dist_final = self.dists_final[-1] + + cost = 0.5 * min_dist + 0.5 * dist_final + # cost = 3 + 2 * (0.5 * min_dist ** 2 + 0.5 * dist_final ** 2) + reward = np.exp(-2 * min_dist) - 1e-5 * action_cost + success = dist_final < 0.05 and min_dist < 0.05 + else: + cost = 0 + reward = - 1e-5 * action_cost + success = False + # action_cost = np.mean(np.sum(np.square(torques), axis=1), axis=0) + + return reward, success, False + + def get_stage_wise_cost(self, ball_in_cup, min_dist, dist_final): #, dist_to_ctxt): + # stop_sim = False + cost = 3 + 2 * (0.5 * min_dist ** 2 + 0.5 * dist_final ** 2) + # if not ball_in_cup: + # # cost = 3 + 2*(0.5 * min_dist + 0.5 * dist_final) + # cost = 3 + 2*(0.5 * min_dist**2 + 0.5 * dist_final**2) + # else: + # # cost = 2*dist_to_ctxt + # cost = 2*dist_to_ctxt**2 + # stop_sim = True + # # print(dist_to_ctxt-0.02) + # print('Context Distance:', dist_to_ctxt) + return cost + + def _get_cost(self, ball_pos, goal_pos, goal_pos_final, u, ctxt, t): + + cost = 0 + if t == self.sim_time*0.8: + dist = 0.5*np.linalg.norm(goal_pos-ball_pos)**2 + 0.5*np.linalg.norm(goal_pos_final-ball_pos)**2 + # dist_ctxt = np.linalg.norm(ctxt-goal_pos)**2 + cost = dist # +dist_ctxt + return cost + + def check_ball_in_cup(self, sim, ball_collision_id): + cup_base_collision_id = sim.model._geom_name2id["cup_base_contact"] + for coni in range(0, sim.data.ncon): + con = sim.data.contact[coni] + + collision = con.geom1 == cup_base_collision_id and con.geom2 == ball_collision_id + collision_trans = con.geom1 == ball_collision_id and con.geom2 == cup_base_collision_id + + if collision or collision_trans: + return True + return False + + def check_collision(self, sim): + for coni in range(0, sim.data.ncon): + con = sim.data.contact[coni] + + collision = con.geom1 in self.collision_ids and con.geom2 == self.ball_collision_id + collision_trans = con.geom1 == self.ball_collision_id and con.geom2 in self.collision_ids + + if collision or collision_trans: + return True + return False diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py new file mode 100644 index 0000000..9161f17 --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py @@ -0,0 +1,210 @@ +from gym.envs.mujoco import mujoco_env +from gym import utils, spaces +import os +import numpy as np +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward +import mujoco_py + + +class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): + def __init__(self, pd_control=True): + self._steps = 0 + self.pd_control = pd_control + + self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", + "ball-in-a-cup_base" + ".xml") + + self.sim_time = 8 # seconds + self.sim_steps = int(self.sim_time / (0.0005 * 4)) # circular dependency.. sim.dt <-> mujocoenv init <-> reward fct + self.reward_function = BallInACupReward(self.sim_steps) + + self.start_pos = np.array([0.0, 0.58760536, 0.0, 1.36004913, 0.0, -0.32072943, -1.57]) + self.start_vel = np.zeros(7) + # self.start_pos = np.array([0.58760536, 1.36004913, -0.32072943]) + self._q_pos = [] + self._q_vel = [] + # self.weight_matrix_scale = 50 + self.p_gains = 1*np.array([200, 300, 100, 100, 10, 10, 2.5]) + self.d_gains = 1*np.array([7, 15, 5, 2.5, 0.3, 0.3, 0.05]) + + self.j_min = np.array([-2.6, -1.985, -2.8, -0.9, -4.55, -1.5707, -2.7]) + self.j_max = np.array([2.6, 1.985, 2.8, 3.14159, 1.25, 1.5707, 2.7]) + + utils.EzPickle.__init__(self) + mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), + frame_skip=4) + + @property + def current_pos(self): + return self.sim.data.qpos[0:7].copy() + + @property + def current_vel(self): + return self.sim.data.qvel[0:7].copy() + + def reset_model(self): + init_pos_all = self.init_qpos.copy() + init_pos_robot = self.start_pos + init_vel = np.zeros_like(init_pos_all) + ball_id = self.sim.model._body_name2id["ball"] + goal_final_id = self.sim.model._site_name2id["cup_goal_final"] + # self.set_state(start_pos, start_vel) + self._steps = 0 + self.reward_function.reset() + self._q_pos = [] + self._q_vel = [] + + # Reset the system + self.sim.data.qpos[:] = init_pos_all + self.sim.data.qvel[:] = init_vel + self.sim.data.qpos[0:7] = init_pos_robot + + self.sim.step() + + self.sim.data.qpos[:] = init_pos_all + self.sim.data.qvel[:] = init_vel + self.sim.data.qpos[0:7] = init_pos_robot + self.sim.data.body_xpos[ball_id, :] = np.copy(self.sim.data.site_xpos[goal_final_id, :]) - np.array([0., 0., 0.329]) + + # Stabilize the system around the initial position + for i in range(0, 2000): + self.sim.data.qpos[7:] = 0. + self.sim.data.qvel[7:] = 0. + # self.sim.data.qpos[7] = -0.2 + cur_pos = self.sim.data.qpos[0:7].copy() + cur_vel = self.sim.data.qvel[0:7].copy() + trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) + self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() + self.sim.step() + # self.render() + + for i in range(0, 2000): + cur_pos = self.sim.data.qpos[0:7].copy() + cur_vel = self.sim.data.qvel[0:7].copy() + trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) + self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() + self.sim.step() + # self.render() + + def do_simulation(self, ctrl, n_frames): + # cur_pos = self.sim.data.qpos[0:7].copy() + # cur_vel = self.sim.data.qvel[0:7].copy() + # des_pos = ctrl[:7] + # des_vel = ctrl[7:] + # trq = self.p_gains * (des_pos - cur_pos) + self.d_gains * (des_vel - cur_vel) + if self.pd_control: + self.sim.data.qfrc_applied[0:7] = ctrl + self.sim.data.qfrc_bias[:7].copy() + else: + self.sim.data.ctrl[:] = ctrl + + for _ in range(n_frames): + try: + self.sim.step() + except mujoco_py.builder.MujocoException as e: + print("Error in simulation: " + str(e)) + # error = True + # Copy the current torque as if it would have been applied until the end of the trajectory + # for i in range(k + 1, sim_time): + # torques.append(trq) + return True + + return False + + def step(self, a): + # Apply gravity compensation + # if not np.all(self.sim.data.qfrc_applied[:7] == self.sim.data.qfrc_bias[:7]): + # self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] + + reward_dist = 0.0 + angular_vel = 0.0 + # if self._steps >= self.steps_before_reward: + # vec = self.get_body_com("fingertip") - self.get_body_com("target") + # reward_dist -= self.reward_weight * np.linalg.norm(vec) + # angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links]) + reward_ctrl = - np.square(a).sum() + # reward_balance = - self.balance_weight * np.abs( + # angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad")) + # + # reward = reward_dist + reward_ctrl + angular_vel + reward_balance + # self.do_simulation(a, self.frame_skip) + + joint_cons_viol = self.check_traj_in_joint_limits() + + crash = self.do_simulation(a, self.frame_skip) + + self._q_pos.append(self.sim.data.qpos[0:7].ravel().copy()) + self._q_vel.append(self.sim.data.qvel[0:7].ravel().copy()) + + ob = self._get_obs() + + if not crash and not joint_cons_viol: + reward, success, collision = self.reward_function.compute_reward(a, self.sim, self._steps) + done = success or self._steps == self.sim_steps - 1 or collision + self._steps += 1 + else: + reward = -1000 + done = True + return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl, + velocity=angular_vel, # reward_balance=reward_balance, + # end_effector=self.get_body_com("fingertip").copy(), + goal=self.goal if hasattr(self, "goal") else None, + traj=self._q_pos) + + def check_traj_in_joint_limits(self): + return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min) + + def check_collision(self): + for coni in range(0, sim.data.ncon): + con = sim.data.contact[coni] + + collision = con.geom1 in self.collision_ids and con.geom2 == self.ball_collision_id + collision_trans = con.geom1 == self.ball_collision_id and con.geom2 in self.collision_ids + + if collision or collision_trans: + return True + return False + + def _get_obs(self): + theta = self.sim.data.qpos.flat[:7] + return np.concatenate([ + np.cos(theta), + np.sin(theta), + # self.get_body_com("target"), # only return target to make problem harder + [self._steps], + ]) + + def extend_des_pos(self, des_pos): + des_pos_full = self.start_pos.copy() + des_pos_full[1] = des_pos[0] + des_pos_full[3] = des_pos[1] + des_pos_full[5] = des_pos[2] + return des_pos_full + + def extend_des_vel(self, des_vel): + des_vel_full = self.start_vel.copy() + des_vel_full[1] = des_vel[0] + des_vel_full[3] = des_vel[1] + des_vel_full[5] = des_vel[2] + return des_vel_full + +if __name__ == "__main__": + env = ALRBallInACupEnv() + env.reset() + env.render() + for i in range(4000): + # objective.load_result("/tmp/cma") + # test with random actions + # ac = 0.1 * env.action_space.sample() + # ac = -np.array([i, i, i]) / 10000 + np.array([env.start_pos[1], env.start_pos[3], env.start_pos[5]]) + ac = np.array([0., -0.1, 0, 0, 0, 0, 0]) + # ac[0] += np.pi/2 + obs, rew, d, info = env.step(ac) + env.render() + + print(rew) + + if d: + break + + env.close() + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/MUJOCO_LOG.TXT b/alr_envs/mujoco/gym_table_tennis/envs/MUJOCO_LOG.TXT new file mode 100644 index 0000000..91c2162 --- /dev/null +++ b/alr_envs/mujoco/gym_table_tennis/envs/MUJOCO_LOG.TXT @@ -0,0 +1,3 @@ +Mon Jan 25 15:45:30 2021 +ERROR: GLEW initalization error: Missing GL version + diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/include_7_motor_actuator.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_7_motor_actuator.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/include_7_motor_actuator.xml diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/include_barrett_wam_7dof_left.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_left.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/include_barrett_wam_7dof_left.xml diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/include_barrett_wam_7dof_right.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/include_barrett_wam_7dof_right.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/include_barrett_wam_7dof_right.xml diff --git 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z%~c`sp-bhuepT{cO3?}D6NL1+i-da4AYE8CdJOD8T)CSn30FC#q>q6L60R5yT91Lm z2d=bHLMP~7X^Kub-!7%UDkRj~C0EKFZ?n=9vVO{vlZtD$>^P=g+T#8L6(n5k+)Z8~ zfiCQi^^~K6gsWe169Wl!VXv*nz~0%_2Rlm5x~E1330Gg@rp%E*m#bGwPG26VAmQq} O+{8cvT{wyw#{UCWK9%MG literal 0 HcmV?d00001 diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/right_arm_actuator.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/right_arm_actuator.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/right_arm_actuator.xml diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/shared.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/shared.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/shared.xml diff --git a/alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml b/alr_envs/mujoco/gym_table_tennis/envs/assets/table_tennis_env.xml similarity index 100% rename from alr_envs/mujoco/gym_table_tennis/envs/robotics/assets/table_tennis/table_tennis_env.xml rename to alr_envs/mujoco/gym_table_tennis/envs/assets/table_tennis_env.xml diff --git a/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py b/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py index 625a122..f155f06 100644 --- a/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py +++ b/alr_envs/mujoco/gym_table_tennis/envs/table_tennis_env.py @@ -3,7 +3,7 @@ from gym import spaces from gym.envs.robotics import robot_env, utils # import xml.etree.ElementTree as ET from alr_envs.mujoco.gym_table_tennis.utils.rewards.hierarchical_reward import HierarchicalRewardTableTennis -# import glfw +import glfw from alr_envs.mujoco.gym_table_tennis.utils.experiment import ball_initialize from pathlib import Path import os @@ -34,18 +34,26 @@ class TableTennisEnv(robot_env.RobotEnv): path_cws = Path.cwd() print(path_cws) current_dir = Path(os.path.split(os.path.realpath(__file__))[0]) - table_tennis_env_xml_path = current_dir / "robotics"/"assets"/"table_tennis"/"table_tennis_env.xml" + table_tennis_env_xml_path = current_dir / "assets"/"table_tennis_env.xml" model_path = str(table_tennis_env_xml_path) self.config = config - action_space = self.config['trajectory']['args']['action_space'] - time_step = self.config['mujoco_sim_env']['args']["time_step"] + action_space = True # self.config['trajectory']['args']['action_space'] + time_step = 0.002 # self.config['mujoco_sim_env']['args']["time_step"] if initial_qpos is None: - initial_qpos = self.config['robot_config']['args']['initial_qpos'] + initial_qpos = {"wam/base_yaw_joint_right": 1.5, + "wam/shoulder_pitch_joint_right": 1, + "wam/shoulder_yaw_joint_right": 0, + "wam/elbow_pitch_joint_right": 1, + "wam/wrist_yaw_joint_right": 1, + "wam/wrist_pitch_joint_right": 0, + "wam/palm_yaw_joint_right": 0} + # initial_qpos = [1.5, 1, 0, 1, 1, 0, 0] # self.config['robot_config']['args']['initial_qpos'] # TODO should read all configuration in config assert initial_qpos is not None, "Must initialize the initial q position of robot arm" n_actions = 7 self.initial_qpos_value = np.array(list(initial_qpos.values())).copy() + # self.initial_qpos_value = np.array(initial_qpos) # # change time step in .xml file # tree = ET.parse(model_path) # root = tree.getroot() @@ -71,6 +79,7 @@ class TableTennisEnv(robot_env.RobotEnv): self.n_actions = n_actions self.action = None self.time_step = time_step + self._dt = time_step self.paddle_center_pos = self.sim.data.get_site_xpos('wam/paddle_center') if reward_obj is None: self.reward_obj = HierarchicalRewardTableTennis() @@ -104,7 +113,8 @@ class TableTennisEnv(robot_env.RobotEnv): self.reward_obj.hitting(self) # if not hitted, return the highest reward if not self.reward_obj.goal_achievement: - return self.reward_obj.highest_reward + # return self.reward_obj.highest_reward + return self.reward_obj.total_reward # # Stage 2 Right Table Contact # self.reward_obj.right_table_contact(self) # if not self.reward_obj.goal_achievement: @@ -119,7 +129,8 @@ class TableTennisEnv(robot_env.RobotEnv): # print("self.reward_obj.highest_reward: ", self.reward_obj.highest_reward) # TODO self.reward_obj.target_achievement(self) - return self.reward_obj.highest_reward + # return self.reward_obj.highest_reward + return self.reward_obj.total_reward def _reset_sim(self): self.sim.set_state(self.initial_state) @@ -217,10 +228,10 @@ if __name__ == '__main__': env.reset() # env.render(mode=render_mode) - for i in range(200): + for i in range(500): # objective.load_result("/tmp/cma") # test with random actions - ac = 2 * env.action_space.sample() + ac = env.action_space.sample() # ac[0] += np.pi/2 obs, rew, d, info = env.step(ac) env.render(mode=render_mode) diff --git a/alr_envs/mujoco/reacher/__init__.py b/alr_envs/mujoco/reacher/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/alr_envs/mujoco/alr_reacher.py b/alr_envs/mujoco/reacher/alr_reacher.py similarity index 80% rename from alr_envs/mujoco/alr_reacher.py rename to alr_envs/mujoco/reacher/alr_reacher.py index 7ae28da..85adf58 100644 --- a/alr_envs/mujoco/alr_reacher.py +++ b/alr_envs/mujoco/reacher/alr_reacher.py @@ -29,9 +29,20 @@ class ALRReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle): else: raise ValueError(f"Invalid number of links {n_links}, only 5 or 7 allowed.") + self._q_pos = [] + self._q_vel = [] + utils.EzPickle.__init__(self) mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2) + @property + def current_pos(self): + return self.sim.data.qpos[0:5].copy() + + @property + def current_vel(self): + return self.sim.data.qvel[0:5].copy() + def step(self, a): self._steps += 1 @@ -47,28 +58,36 @@ class ALRReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle): reward = reward_dist + reward_ctrl + angular_vel + reward_balance self.do_simulation(a, self.frame_skip) + + self._q_pos.append(self.sim.data.qpos[0:5].ravel().copy()) + self._q_vel.append(self.sim.data.qvel[0:5].ravel().copy()) + ob = self._get_obs() done = False return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl, velocity=angular_vel, reward_balance=reward_balance, end_effector=self.get_body_com("fingertip").copy(), - goal=self.goal if hasattr(self, "goal") else None) + goal=self.goal if hasattr(self, "goal") else None, + traj=self._q_pos, vel=self._q_vel) def viewer_setup(self): self.viewer.cam.trackbodyid = 0 def reset_model(self): - qpos = self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos + qpos = self.init_qpos # self.np_random.uniform(low=-0.1, high=0.1, size=self.model.nq) + self.init_qpos while True: self.goal = self.np_random.uniform(low=-self.n_links / 10, high=self.n_links / 10, size=2) if np.linalg.norm(self.goal) < self.n_links / 10: break qpos[-2:] = self.goal - qvel = self.init_qvel + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv) + qvel = self.init_qvel # + self.np_random.uniform(low=-.005, high=.005, size=self.model.nv) qvel[-2:] = 0 self.set_state(qpos, qvel) self._steps = 0 + self._q_pos = [] + self._q_vel = [] + return self._get_obs() def _get_obs(self): diff --git a/alr_envs/mujoco/assets/reacher_5links.xml b/alr_envs/mujoco/reacher/assets/reacher_5links.xml similarity index 100% rename from alr_envs/mujoco/assets/reacher_5links.xml rename to alr_envs/mujoco/reacher/assets/reacher_5links.xml diff --git a/alr_envs/mujoco/assets/reacher_7links.xml b/alr_envs/mujoco/reacher/assets/reacher_7links.xml similarity index 100% rename from alr_envs/mujoco/assets/reacher_7links.xml rename to alr_envs/mujoco/reacher/assets/reacher_7links.xml diff --git a/alr_envs/utils/detpmp_env_wrapper.py b/alr_envs/utils/detpmp_env_wrapper.py new file mode 100644 index 0000000..64986a5 --- /dev/null +++ b/alr_envs/utils/detpmp_env_wrapper.py @@ -0,0 +1,88 @@ +from mp_lib import det_promp +import numpy as np +import gym + + +class DetPMPEnvWrapperBase(gym.Wrapper): + def __init__(self, + env, + num_dof, + num_basis, + width, + start_pos=None, + duration=1, + dt=0.01, + post_traj_time=0., + policy=None, + weights_scale=1): + super(DetPMPEnvWrapperBase, self).__init__(env) + self.num_dof = num_dof + self.num_basis = num_basis + self.dim = num_dof * num_basis + self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, width=width, off=0.01) + weights = np.zeros(shape=(num_basis, num_dof)) + self.pmp.set_weights(duration, weights) + self.weights_scale = weights_scale + + self.duration = duration + self.dt = dt + self.post_traj_steps = int(post_traj_time / dt) + + self.start_pos = start_pos + + self.policy = policy + + def __call__(self, params): + params = np.atleast_2d(params) + observations = [] + rewards = [] + dones = [] + infos = [] + for p in params: + observation, reward, done, info = self.rollout(p) + observations.append(observation) + rewards.append(reward) + dones.append(done) + infos.append(info) + + return np.array(rewards), infos + + def rollout(self, params, render=False): + """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" + raise NotImplementedError + + +class DetPMPEnvWrapperPD(DetPMPEnvWrapperBase): + """ + Wrapper for gym environments which creates a trajectory in joint velocity space + """ + def rollout(self, params, render=False): + params = np.reshape(params, newshape=(self.num_basis, self.num_dof)) * self.weights_scale + self.pmp.set_weights(self.duration, params) + t, des_pos, des_vel, des_acc = self.pmp.compute_trajectory(1/self.dt, 1.) + des_pos += self.start_pos[None, :] + + if self.post_traj_steps > 0: + des_pos = np.vstack([des_pos, np.tile(des_pos[-1, :], [self.post_traj_steps, 1])]) + des_vel = np.vstack([des_vel, np.zeros(shape=(self.post_traj_steps, self.num_dof))]) + + self._trajectory = des_pos + + rews = [] + infos = [] + + self.env.reset() + + for t, pos_vel in enumerate(zip(des_pos, des_vel)): + ac = self.policy.get_action(self.env, pos_vel[0], pos_vel[1]) + obs, rew, done, info = self.env.step(ac) + rews.append(rew) + infos.append(info) + if render: + self.env.render(mode="human") + if done: + break + + reward = np.sum(rews) + + return obs, reward, done, info diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 9b1bdf1..823df83 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -6,19 +6,31 @@ import gym class DmpEnvWrapperBase(gym.Wrapper): - def __init__(self, env, num_dof, num_basis, duration=1, dt=0.01, learn_goal=False): + def __init__(self, + env, + num_dof, + num_basis, + start_pos=None, + final_pos=None, + duration=1, + alpha_phase=2, + dt=0.01, + learn_goal=False, + post_traj_time=0., + policy=None): super(DmpEnvWrapperBase, self).__init__(env) self.num_dof = num_dof self.num_basis = num_basis self.dim = num_dof * num_basis if learn_goal: self.dim += num_dof - self.learn_goal = True + self.learn_goal = learn_goal self.duration = duration # seconds time_steps = int(duration / dt) self.t = np.linspace(0, duration, time_steps) + self.post_traj_steps = int(post_traj_time / dt) - phase_generator = ExpDecayPhaseGenerator(alpha_phase=5, duration=duration) + phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration) basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=self.num_basis) self.dmp = dmps.DMP(num_dof=num_dof, @@ -28,13 +40,18 @@ class DmpEnvWrapperBase(gym.Wrapper): dt=dt ) - self.dmp.dmp_start_pos = env.start_pos.reshape((1, num_dof)) + self.dmp.dmp_start_pos = start_pos.reshape((1, num_dof)) dmp_weights = np.zeros((num_basis, num_dof)) - dmp_goal_pos = np.zeros(num_dof) + if learn_goal: + dmp_goal_pos = np.zeros(num_dof) + else: + dmp_goal_pos = final_pos self.dmp.set_weights(dmp_weights, dmp_goal_pos) + self.policy = policy + def __call__(self, params): params = np.atleast_2d(params) observations = [] @@ -48,7 +65,7 @@ class DmpEnvWrapperBase(gym.Wrapper): dones.append(done) infos.append(info) - return np.array(rewards) + return np.array(rewards), infos def goal_and_weights(self, params): if len(params.shape) > 1: @@ -71,7 +88,7 @@ class DmpEnvWrapperBase(gym.Wrapper): raise NotImplementedError -class DmpEnvWrapperAngle(DmpEnvWrapperBase): +class DmpEnvWrapperPos(DmpEnvWrapperBase): """ Wrapper for gym environments which creates a trajectory in joint angle space """ @@ -80,7 +97,12 @@ class DmpEnvWrapperAngle(DmpEnvWrapperBase): if hasattr(self.env, "weight_matrix_scale"): weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) - trajectory, velocities = self.dmp.reference_trajectory(self.t) + trajectory, _ = self.dmp.reference_trajectory(self.t) + + if self.post_traj_steps > 0: + trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])]) + + self._trajectory = trajectory rews = [] @@ -95,8 +117,6 @@ class DmpEnvWrapperAngle(DmpEnvWrapperBase): break reward = np.sum(rews) - # done = True - info = {} return obs, reward, done, info @@ -110,7 +130,7 @@ class DmpEnvWrapperVel(DmpEnvWrapperBase): if hasattr(self.env, "weight_matrix_scale"): weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) - trajectory, velocities = self.dmp.reference_trajectory(self.t) + _, velocities = self.dmp.reference_trajectory(self.t) rews = [] infos = [] @@ -129,3 +149,41 @@ class DmpEnvWrapperVel(DmpEnvWrapperBase): reward = np.sum(rews) return obs, reward, done, info + + +class DmpEnvWrapperPD(DmpEnvWrapperBase): + """ + Wrapper for gym environments which creates a trajectory in joint velocity space + """ + def rollout(self, action, render=False): + goal_pos, weight_matrix = self.goal_and_weights(action) + if hasattr(self.env, "weight_matrix_scale"): + weight_matrix = weight_matrix * self.env.weight_matrix_scale + self.dmp.set_weights(weight_matrix, goal_pos) + trajectory, velocity = self.dmp.reference_trajectory(self.t) + + if self.post_traj_steps > 0: + trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])]) + velocity = np.vstack([velocity, np.zeros(shape=(self.post_traj_steps, self.num_dof))]) + + self._trajectory = trajectory + self._velocity = velocity + + rews = [] + infos = [] + + self.env.reset() + + for t, pos_vel in enumerate(zip(trajectory, velocity)): + ac = self.policy.get_action(self.env, pos_vel[0], pos_vel[1]) + obs, rew, done, info = self.env.step(ac) + rews.append(rew) + infos.append(info) + if render: + self.env.render(mode="human") + if done: + break + + reward = np.sum(rews) + + return obs, reward, done, info diff --git a/alr_envs/utils/policies.py b/alr_envs/utils/policies.py new file mode 100644 index 0000000..5d251fa --- /dev/null +++ b/alr_envs/utils/policies.py @@ -0,0 +1,15 @@ +class PDController: + def __init__(self, p_gains, d_gains): + self.p_gains = p_gains + self.d_gains = d_gains + + def get_action(self, env, des_pos, des_vel): + # TODO: make standardized ALRenv such that all of them have current_pos/vel attributes + cur_pos = env.current_pos + cur_vel = env.current_vel + if len(des_pos) != len(cur_pos): + des_pos = env.extend_des_pos(des_pos) + if len(des_vel) != len(cur_vel): + des_vel = env.extend_des_vel(des_vel) + trq = self.p_gains * (des_pos - cur_pos) + self.d_gains * (des_vel - cur_vel) + return trq diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py index 9d699be..fcde324 100644 --- a/dmp_env_wrapper_example.py +++ b/dmp_env_wrapper_example.py @@ -1,36 +1,9 @@ from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperVel from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker from alr_envs.classic_control.hole_reacher import HoleReacher -from gym.vector.async_vector_env import AsyncVectorEnv import numpy as np -# env = gym.make('alr_envs:SimpleReacher-v0') -# env = HoleReacher(num_links=5, -# allow_self_collision=False, -# allow_wall_collision=True, -# hole_width=0.15, -# hole_depth=1, -# hole_x=1) -# -# env = DmpEnvWrapperVel(env, -# num_dof=5, -# num_basis=5, -# duration=2, -# dt=env._dt, -# learn_goal=True) -# -# params = np.hstack([50 * np.random.randn(25), np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4])]) -# -# print(params) -# -# env.reset() -# obs, rew, done, info = env.step(params, render=True) -# -# print(env.env._joint_angles) -# -# print(rew) - if __name__ == "__main__": def make_env(rank, seed=0): diff --git a/dmp_pd_control_example.py b/dmp_pd_control_example.py new file mode 100644 index 0000000..29e7cda --- /dev/null +++ b/dmp_pd_control_example.py @@ -0,0 +1,48 @@ +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperPD +from alr_envs.utils.policies import PDController +from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker +from alr_envs.mujoco.reacher.alr_reacher import ALRReacherEnv +import numpy as np + + +if __name__ == "__main__": + + def make_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the inital seed for RNG + :param rank: (int) index of the subprocess + """ + def _init(): + p_gains = np.array([100, 100, 100, 100, 100]) + d_gains = np.array([1, 1, 1, 1, 1]) + policy = PDController(p_gains, d_gains) + + env = ALRReacherEnv() + + env = DmpEnvWrapperPD(env, + num_dof=5, + num_basis=5, + duration=4, + dt=env.dt, + learn_goal=False, + start_pos=env.init_qpos[:5], + final_pos=env.init_qpos[:5], + alpha_phase=2, + policy=policy + ) + env.seed(seed + rank) + return env + return _init + + dim = 25 + env = make_env(0, 0)() + + params = 10 * np.random.randn(dim) + + out = env.rollout(params, render=True) + + print(out) From c81378b9e754927f871f71e81e0565a8f5026e2c Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 11 Feb 2021 10:49:57 +0100 Subject: [PATCH 07/43] support for contexts, policy classes, pd controller example, breaking changes etc --- alr_envs/mujoco/alr_mujoco_env.py | 247 ++++++++++++ alr_envs/mujoco/alr_reward_fct.py | 21 + .../mujoco/ball_in_a_cup/assets/biac_base.xml | 360 ++++++++++++++++++ .../ball_in_a_cup/ball_in_a_cup_reward.py | 44 +-- .../ball_in_a_cup/ball_in_a_cup_simple.py | 155 +++----- alr_envs/utils/dmp_async_vec_env.py | 32 +- alr_envs/utils/dmp_env_wrapper.py | 110 ++---- alr_envs/utils/policies.py | 40 +- dmp_pd_control_example.py | 60 +-- 9 files changed, 807 insertions(+), 262 deletions(-) create mode 100644 alr_envs/mujoco/alr_mujoco_env.py create mode 100644 alr_envs/mujoco/alr_reward_fct.py create mode 100644 alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml diff --git a/alr_envs/mujoco/alr_mujoco_env.py b/alr_envs/mujoco/alr_mujoco_env.py new file mode 100644 index 0000000..c94beeb --- /dev/null +++ b/alr_envs/mujoco/alr_mujoco_env.py @@ -0,0 +1,247 @@ +from collections import OrderedDict +import os + + +from gym import error, spaces +from gym.utils import seeding +import numpy as np +from os import path +import gym + +try: + import mujoco_py +except ImportError as e: + raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e)) + +DEFAULT_SIZE = 500 + + +def convert_observation_to_space(observation): + if isinstance(observation, dict): + space = spaces.Dict(OrderedDict([ + (key, convert_observation_to_space(value)) + for key, value in observation.items() + ])) + elif isinstance(observation, np.ndarray): + low = np.full(observation.shape, -float('inf'), dtype=np.float32) + high = np.full(observation.shape, float('inf'), dtype=np.float32) + space = spaces.Box(low, high, dtype=observation.dtype) + else: + raise NotImplementedError(type(observation), observation) + + return space + + +class AlrMujocoEnv(gym.Env): + """ + Superclass for all MuJoCo environments. + """ + + def __init__(self, model_path, n_substeps, apply_gravity_comp=True): + """ + + Args: + model_path: path to xml file + n_substeps: how many steps mujoco does per call to env.step + use_servo: use actuator defined in xml, use False for direct torque control + """ + if model_path.startswith("/"): + fullpath = model_path + else: + fullpath = os.path.join(os.path.dirname(__file__), "assets", model_path) + if not path.exists(fullpath): + raise IOError("File %s does not exist" % fullpath) + self.n_substeps = n_substeps + self.apply_gravity_comp = apply_gravity_comp + self.model = mujoco_py.load_model_from_path(fullpath) + self.sim = mujoco_py.MjSim(self.model, nsubsteps=n_substeps) + self.data = self.sim.data + self.viewer = None + self._viewers = {} + + self.metadata = { + 'render.modes': ['human', 'rgb_array', 'depth_array'], + 'video.frames_per_second': int(np.round(1.0 / self.dt)) + } + + self.init_qpos = self.sim.data.qpos.ravel().copy() + self.init_qvel = self.sim.data.qvel.ravel().copy() + + self._set_action_space() + + # action = self.action_space.sample() + # observation, _reward, done, _info = self.step(action) + # assert not done + + observation = self.reset() + + self._set_observation_space(observation) + + self.seed() + + @property + def current_pos(self): + """ + By default returns the joint positions of all simulated objects. May be overriden in subclass. + """ + return self.sim.data.qpos + + @property + def current_vel(self): + """ + By default returns the joint velocities of all simulated objects. May be overriden in subclass. + """ + return self.sim.data.qvel + + def extend_des_pos(self, des_pos): + """ + In a simplified environment, the actions may only control a subset of all the joints. + Extend the trajectory to match the environments full action space + Args: + des_pos: + + Returns: + + """ + pass + + def extend_des_vel(self, des_vel): + pass + + def _set_action_space(self): + bounds = self.model.actuator_ctrlrange.copy().astype(np.float32) + low, high = bounds.T + self.action_space = spaces.Box(low=low, high=high, dtype=np.float32) + return self.action_space + + def _set_observation_space(self, observation): + self.observation_space = convert_observation_to_space(observation) + return self.observation_space + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + # methods to override: + # ---------------------------- + + def configure(self, *args, **kwargs): + """ + Helper method to set certain environment properties such as contexts in contextual environments since reset() + doesn't take arguments. Should be called before/after reset(). TODO: before or after? + """ + pass + + def reset_model(self): + """ + Reset the robot degrees of freedom (qpos and qvel). + Implement this in each subclass. + """ + raise NotImplementedError + + def viewer_setup(self): + """ + This method is called when the viewer is initialized. + Optionally implement this method, if you need to tinker with camera position + and so forth. + """ + pass + + # ----------------------------- + + def reset(self): + self.sim.reset() + ob = self.reset_model() + return ob + + def set_state(self, qpos, qvel): + assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,) + old_state = self.sim.get_state() + new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel, + old_state.act, old_state.udd_state) + self.sim.set_state(new_state) + self.sim.forward() + + @property + def dt(self): + return self.model.opt.timestep * self.n_substeps + + def do_simulation(self, ctrl): + """ + Additionally returns whether there was an error while stepping the simulation + """ + error_in_sim = False + num_actuations = len(ctrl) + if self.apply_gravity_comp: + self.sim.data.ctrl[:num_actuations] = ctrl + self.sim.data.qfrc_bias[:num_actuations].copy() / self.model.actuator_gear[:, 0] + else: + self.sim.data.ctrl[:num_actuations] = ctrl + + try: + self.sim.step() + except mujoco_py.builder.MujocoException as e: + error_in_sim = True + + return error_in_sim + + def render(self, + mode='human', + width=DEFAULT_SIZE, + height=DEFAULT_SIZE, + camera_id=None, + camera_name=None): + if mode == 'rgb_array' or mode == 'depth_array': + if camera_id is not None and camera_name is not None: + raise ValueError("Both `camera_id` and `camera_name` cannot be" + " specified at the same time.") + + no_camera_specified = camera_name is None and camera_id is None + if no_camera_specified: + camera_name = 'track' + + if camera_id is None and camera_name in self.model._camera_name2id: + camera_id = self.model.camera_name2id(camera_name) + + self._get_viewer(mode).render(width, height, camera_id=camera_id) + + if mode == 'rgb_array': + # window size used for old mujoco-py: + data = self._get_viewer(mode).read_pixels(width, height, depth=False) + # original image is upside-down, so flip it + return data[::-1, :, :] + elif mode == 'depth_array': + self._get_viewer(mode).render(width, height) + # window size used for old mujoco-py: + # Extract depth part of the read_pixels() tuple + data = self._get_viewer(mode).read_pixels(width, height, depth=True)[1] + # original image is upside-down, so flip it + return data[::-1, :] + elif mode == 'human': + self._get_viewer(mode).render() + + def close(self): + if self.viewer is not None: + # self.viewer.finish() + self.viewer = None + self._viewers = {} + + def _get_viewer(self, mode): + self.viewer = self._viewers.get(mode) + if self.viewer is None: + if mode == 'human': + self.viewer = mujoco_py.MjViewer(self.sim) + elif mode == 'rgb_array' or mode == 'depth_array': + self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, -1) + + self.viewer_setup() + self._viewers[mode] = self.viewer + return self.viewer + + def get_body_com(self, body_name): + return self.data.get_body_xpos(body_name) + + def state_vector(self): + return np.concatenate([ + self.sim.data.qpos.flat, + self.sim.data.qvel.flat + ]) diff --git a/alr_envs/mujoco/alr_reward_fct.py b/alr_envs/mujoco/alr_reward_fct.py new file mode 100644 index 0000000..c96dd07 --- /dev/null +++ b/alr_envs/mujoco/alr_reward_fct.py @@ -0,0 +1,21 @@ +class AlrReward: + """ + A base class for non-Markovian reward functions which may need trajectory information to calculate an episodic + reward. Call the methods in reset() and step() of the environment. + """ + + # methods to override: + # ---------------------------- + def reset(self, *args, **kwargs): + """ + Reset the reward function, empty state buffers before an episode, set contexts that influence reward, etc. + """ + raise NotImplementedError + + def compute_reward(self, *args, **kwargs): + """ + + Returns: Useful things to return are reward values, success flags or crash flags + + """ + raise NotImplementedError diff --git a/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml new file mode 100644 index 0000000..c8c2643 --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml @@ -0,0 +1,360 @@ + + + diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index 69932db..32310b3 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -1,7 +1,8 @@ import numpy as np +from alr_envs.mujoco import alr_reward_fct -class BallInACupReward: +class BallInACupReward(alr_reward_fct.AlrReward): def __init__(self, sim_time): self.sim_time = sim_time @@ -14,7 +15,6 @@ class BallInACupReward: "forearm_link_convex_decomposition_p1_geom", "forearm_link_convex_decomposition_p2_geom"] - self.ctxt_id = None self.ball_id = None self.ball_collision_id = None self.goal_id = None @@ -34,8 +34,7 @@ class BallInACupReward: self.dists_final = [] self.costs = [] - def compute_reward(self, action, sim, step): - self.ctxt_id = sim.model._site_name2id['context_point'] + def compute_reward(self, action, sim, step, context=None): self.ball_id = sim.model._body_name2id["ball"] self.ball_collision_id = sim.model._geom_name2id["ball_geom"] self.goal_id = sim.model._site_name2id["cup_goal"] @@ -50,59 +49,26 @@ class BallInACupReward: goal_final_pos = sim.data.site_xpos[self.goal_final_id] self.dists.append(np.linalg.norm(goal_pos - ball_pos)) self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) - # dists_ctxt.append(np.linalg.norm(ball_pos - ctxt)) self.ball_traj[step, :] = ball_pos if self.check_collision(sim): return -1000, False, True - # self._get_cost(ball_pos, goal_pos, goal_final_pos, action, - # sim.data.get_site_xpos('context_point').copy(), step) - - # min_dist = np.min(self.dists) - # dist_final = self.dists_final[-1] action_cost = np.sum(np.square(action)) - # cost = self.get_stage_wise_cost(ball_in_cup, min_dist, self.dists_final[-1]) # , self.dists_ctxt[-1]) if step == self.sim_time - 1: min_dist = np.min(self.dists) dist_final = self.dists_final[-1] cost = 0.5 * min_dist + 0.5 * dist_final - # cost = 3 + 2 * (0.5 * min_dist ** 2 + 0.5 * dist_final ** 2) - reward = np.exp(-2 * min_dist) - 1e-5 * action_cost - success = dist_final < 0.05 and min_dist < 0.05 + reward = np.exp(-2 * cost) - 1e-5 * action_cost + success = dist_final < 0.05 and ball_in_cup else: - cost = 0 reward = - 1e-5 * action_cost success = False - # action_cost = np.mean(np.sum(np.square(torques), axis=1), axis=0) return reward, success, False - def get_stage_wise_cost(self, ball_in_cup, min_dist, dist_final): #, dist_to_ctxt): - # stop_sim = False - cost = 3 + 2 * (0.5 * min_dist ** 2 + 0.5 * dist_final ** 2) - # if not ball_in_cup: - # # cost = 3 + 2*(0.5 * min_dist + 0.5 * dist_final) - # cost = 3 + 2*(0.5 * min_dist**2 + 0.5 * dist_final**2) - # else: - # # cost = 2*dist_to_ctxt - # cost = 2*dist_to_ctxt**2 - # stop_sim = True - # # print(dist_to_ctxt-0.02) - # print('Context Distance:', dist_to_ctxt) - return cost - - def _get_cost(self, ball_pos, goal_pos, goal_pos_final, u, ctxt, t): - - cost = 0 - if t == self.sim_time*0.8: - dist = 0.5*np.linalg.norm(goal_pos-ball_pos)**2 + 0.5*np.linalg.norm(goal_pos_final-ball_pos)**2 - # dist_ctxt = np.linalg.norm(ctxt-goal_pos)**2 - cost = dist # +dist_ctxt - return cost - def check_ball_in_cup(self, sim, ball_collision_id): cup_base_collision_id = sim.model._geom_name2id["cup_base_contact"] for coni in range(0, sim.data.ncon): diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py index 9161f17..1a25211 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py @@ -1,38 +1,41 @@ -from gym.envs.mujoco import mujoco_env +from alr_envs.mujoco import alr_mujoco_env from gym import utils, spaces import os import numpy as np from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward -import mujoco_py -class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): - def __init__(self, pd_control=True): +class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): + def __init__(self, reward_function=None): self._steps = 0 - self.pd_control = pd_control self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", - "ball-in-a-cup_base" + ".xml") + "biac_base" + ".xml") self.sim_time = 8 # seconds self.sim_steps = int(self.sim_time / (0.0005 * 4)) # circular dependency.. sim.dt <-> mujocoenv init <-> reward fct - self.reward_function = BallInACupReward(self.sim_steps) + self.reward_function = reward_function(self.sim_steps) self.start_pos = np.array([0.0, 0.58760536, 0.0, 1.36004913, 0.0, -0.32072943, -1.57]) self.start_vel = np.zeros(7) - # self.start_pos = np.array([0.58760536, 1.36004913, -0.32072943]) + self._q_pos = [] self._q_vel = [] # self.weight_matrix_scale = 50 - self.p_gains = 1*np.array([200, 300, 100, 100, 10, 10, 2.5]) - self.d_gains = 1*np.array([7, 15, 5, 2.5, 0.3, 0.3, 0.05]) + self.max_ctrl = np.array([150., 125., 40., 60., 5., 5., 2.]) + self.p_gains = 1 / self.max_ctrl * np.array([200, 300, 100, 100, 10, 10, 2.5]) + self.d_gains = 1 / self.max_ctrl * np.array([7, 15, 5, 2.5, 0.3, 0.3, 0.05]) self.j_min = np.array([-2.6, -1.985, -2.8, -0.9, -4.55, -1.5707, -2.7]) self.j_max = np.array([2.6, 1.985, 2.8, 3.14159, 1.25, 1.5707, 2.7]) + self.context = None + utils.EzPickle.__init__(self) - mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), - frame_skip=4) + alr_mujoco_env.AlrMujocoEnv.__init__(self, + self.xml_path, + apply_gravity_comp=True, + n_substeps=4) @property def current_pos(self): @@ -42,96 +45,68 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): def current_vel(self): return self.sim.data.qvel[0:7].copy() + def configure(self, context): + self.context = context + def reset_model(self): init_pos_all = self.init_qpos.copy() init_pos_robot = self.start_pos init_vel = np.zeros_like(init_pos_all) ball_id = self.sim.model._body_name2id["ball"] goal_final_id = self.sim.model._site_name2id["cup_goal_final"] - # self.set_state(start_pos, start_vel) + self._steps = 0 self.reward_function.reset() self._q_pos = [] self._q_vel = [] + start_pos = init_pos_all + start_pos[0:7] = init_pos_robot + + self.set_state(start_pos, init_vel) + # Reset the system - self.sim.data.qpos[:] = init_pos_all - self.sim.data.qvel[:] = init_vel - self.sim.data.qpos[0:7] = init_pos_robot + # self.sim.data.qpos[:] = init_pos_all + # self.sim.data.qvel[:] = init_vel + # self.sim.data.qpos[0:7] = init_pos_robot + # + # self.sim.step() + # + # self.sim.data.qpos[:] = init_pos_all + # self.sim.data.qvel[:] = init_vel + # self.sim.data.qpos[0:7] = init_pos_robot + # self.sim.data.body_xpos[ball_id, :] = np.copy(self.sim.data.site_xpos[goal_final_id, :]) - np.array([0., 0., 0.329]) + # + # # Stabilize the system around the initial position + # for i in range(0, 500): + # self.sim.data.qpos[7:] = 0. + # self.sim.data.qvel[7:] = 0. + # # self.sim.data.qpos[7] = -0.2 + # cur_pos = self.sim.data.qpos[0:7].copy() + # cur_vel = self.sim.data.qvel[0:7].copy() + # trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) + # self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() + # self.sim.step() + # self.render() + # + # for i in range(0, 500): + # cur_pos = self.sim.data.qpos[0:7].copy() + # cur_vel = self.sim.data.qvel[0:7].copy() + # trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) + # self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() + # self.sim.step() + # self.render() - self.sim.step() - - self.sim.data.qpos[:] = init_pos_all - self.sim.data.qvel[:] = init_vel - self.sim.data.qpos[0:7] = init_pos_robot - self.sim.data.body_xpos[ball_id, :] = np.copy(self.sim.data.site_xpos[goal_final_id, :]) - np.array([0., 0., 0.329]) - - # Stabilize the system around the initial position - for i in range(0, 2000): - self.sim.data.qpos[7:] = 0. - self.sim.data.qvel[7:] = 0. - # self.sim.data.qpos[7] = -0.2 - cur_pos = self.sim.data.qpos[0:7].copy() - cur_vel = self.sim.data.qvel[0:7].copy() - trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) - self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() - self.sim.step() - # self.render() - - for i in range(0, 2000): - cur_pos = self.sim.data.qpos[0:7].copy() - cur_vel = self.sim.data.qvel[0:7].copy() - trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) - self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() - self.sim.step() - # self.render() - - def do_simulation(self, ctrl, n_frames): - # cur_pos = self.sim.data.qpos[0:7].copy() - # cur_vel = self.sim.data.qvel[0:7].copy() - # des_pos = ctrl[:7] - # des_vel = ctrl[7:] - # trq = self.p_gains * (des_pos - cur_pos) + self.d_gains * (des_vel - cur_vel) - if self.pd_control: - self.sim.data.qfrc_applied[0:7] = ctrl + self.sim.data.qfrc_bias[:7].copy() - else: - self.sim.data.ctrl[:] = ctrl - - for _ in range(n_frames): - try: - self.sim.step() - except mujoco_py.builder.MujocoException as e: - print("Error in simulation: " + str(e)) - # error = True - # Copy the current torque as if it would have been applied until the end of the trajectory - # for i in range(k + 1, sim_time): - # torques.append(trq) - return True - - return False + return self._get_obs() def step(self, a): - # Apply gravity compensation - # if not np.all(self.sim.data.qfrc_applied[:7] == self.sim.data.qfrc_bias[:7]): - # self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] - reward_dist = 0.0 angular_vel = 0.0 - # if self._steps >= self.steps_before_reward: - # vec = self.get_body_com("fingertip") - self.get_body_com("target") - # reward_dist -= self.reward_weight * np.linalg.norm(vec) - # angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links]) reward_ctrl = - np.square(a).sum() - # reward_balance = - self.balance_weight * np.abs( - # angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad")) - # - # reward = reward_dist + reward_ctrl + angular_vel + reward_balance - # self.do_simulation(a, self.frame_skip) + crash = self.do_simulation(a) joint_cons_viol = self.check_traj_in_joint_limits() - crash = self.do_simulation(a, self.frame_skip) - self._q_pos.append(self.sim.data.qpos[0:7].ravel().copy()) self._q_vel.append(self.sim.data.qvel[0:7].ravel().copy()) @@ -153,17 +128,7 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): def check_traj_in_joint_limits(self): return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min) - def check_collision(self): - for coni in range(0, sim.data.ncon): - con = sim.data.contact[coni] - - collision = con.geom1 in self.collision_ids and con.geom2 == self.ball_collision_id - collision_trans = con.geom1 == self.ball_collision_id and con.geom2 in self.collision_ids - - if collision or collision_trans: - return True - return False - + # TODO: extend observation space def _get_obs(self): theta = self.sim.data.qpos.flat[:7] return np.concatenate([ @@ -187,8 +152,9 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): des_vel_full[5] = des_vel[2] return des_vel_full + if __name__ == "__main__": - env = ALRBallInACupEnv() + env = ALRBallInACupEnv(reward_function=BallInACupReward) env.reset() env.render() for i in range(4000): @@ -196,7 +162,8 @@ if __name__ == "__main__": # test with random actions # ac = 0.1 * env.action_space.sample() # ac = -np.array([i, i, i]) / 10000 + np.array([env.start_pos[1], env.start_pos[3], env.start_pos[5]]) - ac = np.array([0., -0.1, 0, 0, 0, 0, 0]) + # ac = np.array([0., -10, 0, -1, 0, 1, 0]) + ac = np.array([0.,0., 0, 0, 0, 0, 0]) # ac[0] += np.pi/2 obs, rew, d, info = env.step(ac) env.render() diff --git a/alr_envs/utils/dmp_async_vec_env.py b/alr_envs/utils/dmp_async_vec_env.py index 56f4ca7..771b0d8 100644 --- a/alr_envs/utils/dmp_async_vec_env.py +++ b/alr_envs/utils/dmp_async_vec_env.py @@ -24,14 +24,14 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): n=n_samples, fn=np.zeros) - def __call__(self, params): - return self.rollout(params) + def __call__(self, params, contexts=None): + return self.rollout(params, contexts) - def rollout_async(self, actions): + def rollout_async(self, params, contexts): """ Parameters ---------- - actions : iterable of samples from `action_space` + params : iterable of samples from `action_space` List of actions. """ self._assert_is_running() @@ -40,11 +40,17 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): 'for a pending call to `{0}` to complete.'.format( self._state.value), self._state.value) - actions = np.atleast_2d(actions) - split_actions = np.array_split(actions, np.minimum(len(actions), self.num_envs)) - for pipe, action in zip(self.parent_pipes, split_actions): - pipe.send(('rollout', action)) - for pipe in self.parent_pipes[len(split_actions):]: + params = np.atleast_2d(params) + split_params = np.array_split(params, np.minimum(len(params), self.num_envs)) + if contexts is None: + split_contexts = np.array_split([None, ] * len(params), np.minimum(len(params), self.num_envs)) + else: + split_contexts = np.array_split(contexts, np.minimum(len(contexts), self.num_envs)) + + assert np.all([len(p) == len(c) for p, c in zip(split_params, split_contexts)]) + for pipe, param, context in zip(self.parent_pipes, split_params, split_contexts): + pipe.send(('rollout', (param, context))) + for pipe in self.parent_pipes[len(split_params):]: pipe.send(('idle', None)) self._state = AsyncState.WAITING_ROLLOUT @@ -98,8 +104,8 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): return np.array(rewards), infos - def rollout(self, actions): - self.rollout_async(actions) + def rollout(self, actions, contexts): + self.rollout_async(actions, contexts) return self.rollout_wait() @@ -123,8 +129,8 @@ def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): rewards = [] dones = [] infos = [] - for d in data: - observation, reward, done, info = env.rollout(d) + for p, c in zip(*data): + observation, reward, done, info = env.rollout(p, c) observations.append(observation) rewards.append(reward) dones.append(done) diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 823df83..3b461f7 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -5,7 +5,19 @@ import numpy as np import gym -class DmpEnvWrapperBase(gym.Wrapper): +def get_policy_class(policy_type): + if policy_type == "motor": + from alr_envs.utils.policies import PDController + return PDController + elif policy_type == "velocity": + from alr_envs.utils.policies import VelController + return VelController + elif policy_type == "position": + from alr_envs.utils.policies import PosController + return PosController + + +class DmpEnvWrapper(gym.Wrapper): def __init__(self, env, num_dof, @@ -17,8 +29,9 @@ class DmpEnvWrapperBase(gym.Wrapper): dt=0.01, learn_goal=False, post_traj_time=0., - policy=None): - super(DmpEnvWrapperBase, self).__init__(env) + policy_type=None, + weights_scale=1.): + super(DmpEnvWrapper, self).__init__(env) self.num_dof = num_dof self.num_basis = num_basis self.dim = num_dof * num_basis @@ -49,17 +62,19 @@ class DmpEnvWrapperBase(gym.Wrapper): dmp_goal_pos = final_pos self.dmp.set_weights(dmp_weights, dmp_goal_pos) + self.weights_scale = weights_scale - self.policy = policy + policy_class = get_policy_class(policy_type) + self.policy = policy_class(env) - def __call__(self, params): + def __call__(self, params, contexts=None): params = np.atleast_2d(params) observations = [] rewards = [] dones = [] infos = [] - for p in params: - observation, reward, done, info = self.rollout(p) + for p, c in zip(params, contexts): + observation, reward, done, info = self.rollout(p, c) observations.append(observation) rewards.append(reward) dones.append(done) @@ -81,82 +96,11 @@ class DmpEnvWrapperBase(gym.Wrapper): goal_pos = None weight_matrix = np.reshape(params, [self.num_basis, self.num_dof]) - return goal_pos, weight_matrix + return goal_pos, weight_matrix * self.weights_scale - def rollout(self, params, render=False): + def rollout(self, params, context=None, render=False): """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" - raise NotImplementedError - - -class DmpEnvWrapperPos(DmpEnvWrapperBase): - """ - Wrapper for gym environments which creates a trajectory in joint angle space - """ - def rollout(self, action, render=False): - goal_pos, weight_matrix = self.goal_and_weights(action) - if hasattr(self.env, "weight_matrix_scale"): - weight_matrix = weight_matrix * self.env.weight_matrix_scale - self.dmp.set_weights(weight_matrix, goal_pos) - trajectory, _ = self.dmp.reference_trajectory(self.t) - - if self.post_traj_steps > 0: - trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])]) - - self._trajectory = trajectory - - rews = [] - - self.env.reset() - - for t, traj in enumerate(trajectory): - obs, rew, done, info = self.env.step(traj) - rews.append(rew) - if render: - self.env.render(mode="human") - if done: - break - - reward = np.sum(rews) - - return obs, reward, done, info - - -class DmpEnvWrapperVel(DmpEnvWrapperBase): - """ - Wrapper for gym environments which creates a trajectory in joint velocity space - """ - def rollout(self, action, render=False): - goal_pos, weight_matrix = self.goal_and_weights(action) - if hasattr(self.env, "weight_matrix_scale"): - weight_matrix = weight_matrix * self.env.weight_matrix_scale - self.dmp.set_weights(weight_matrix, goal_pos) - _, velocities = self.dmp.reference_trajectory(self.t) - - rews = [] - infos = [] - - self.env.reset() - - for t, vel in enumerate(velocities): - obs, rew, done, info = self.env.step(vel) - rews.append(rew) - infos.append(info) - if render: - self.env.render(mode="human") - if done: - break - - reward = np.sum(rews) - - return obs, reward, done, info - - -class DmpEnvWrapperPD(DmpEnvWrapperBase): - """ - Wrapper for gym environments which creates a trajectory in joint velocity space - """ - def rollout(self, action, render=False): - goal_pos, weight_matrix = self.goal_and_weights(action) + goal_pos, weight_matrix = self.goal_and_weights(params) if hasattr(self.env, "weight_matrix_scale"): weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) @@ -173,9 +117,11 @@ class DmpEnvWrapperPD(DmpEnvWrapperBase): infos = [] self.env.reset() + if context is not None: + self.env.configure(context) for t, pos_vel in enumerate(zip(trajectory, velocity)): - ac = self.policy.get_action(self.env, pos_vel[0], pos_vel[1]) + ac = self.policy.get_action(pos_vel[0], pos_vel[1]) obs, rew, done, info = self.env.step(ac) rews.append(rew) infos.append(info) diff --git a/alr_envs/utils/policies.py b/alr_envs/utils/policies.py index 5d251fa..0a4481c 100644 --- a/alr_envs/utils/policies.py +++ b/alr_envs/utils/policies.py @@ -1,15 +1,37 @@ -class PDController: - def __init__(self, p_gains, d_gains): - self.p_gains = p_gains - self.d_gains = d_gains +from alr_envs.mujoco.alr_mujoco_env import AlrMujocoEnv - def get_action(self, env, des_pos, des_vel): + +class BaseController: + def __init__(self, env: AlrMujocoEnv): + self.env = env + + def get_action(self, des_pos, des_vel): + raise NotImplementedError + + +class PosController(BaseController): + def get_action(self, des_pos, des_vel): + return des_pos + + +class VelController(BaseController): + def get_action(self, des_pos, des_vel): + return des_vel + + +class PDController(BaseController): + def __init__(self, env): + self.p_gains = env.p_gains + self.d_gains = env.d_gains + super(PDController, self).__init__(env) + + def get_action(self, des_pos, des_vel): # TODO: make standardized ALRenv such that all of them have current_pos/vel attributes - cur_pos = env.current_pos - cur_vel = env.current_vel + cur_pos = self.env.current_pos + cur_vel = self.env.current_vel if len(des_pos) != len(cur_pos): - des_pos = env.extend_des_pos(des_pos) + des_pos = self.env.extend_des_pos(des_pos) if len(des_vel) != len(cur_vel): - des_vel = env.extend_des_vel(des_vel) + des_vel = self.env.extend_des_vel(des_vel) trq = self.p_gains * (des_pos - cur_pos) + self.d_gains * (des_vel - cur_vel) return trq diff --git a/dmp_pd_control_example.py b/dmp_pd_control_example.py index 29e7cda..9c4bec0 100644 --- a/dmp_pd_control_example.py +++ b/dmp_pd_control_example.py @@ -1,7 +1,7 @@ -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperPD -from alr_envs.utils.policies import PDController +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker -from alr_envs.mujoco.reacher.alr_reacher import ALRReacherEnv +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward import numpy as np @@ -17,32 +17,42 @@ if __name__ == "__main__": :param rank: (int) index of the subprocess """ def _init(): - p_gains = np.array([100, 100, 100, 100, 100]) - d_gains = np.array([1, 1, 1, 1, 1]) - policy = PDController(p_gains, d_gains) + _env = ALRBallInACupEnv(BallInACupReward) - env = ALRReacherEnv() - - env = DmpEnvWrapperPD(env, - num_dof=5, - num_basis=5, - duration=4, - dt=env.dt, - learn_goal=False, - start_pos=env.init_qpos[:5], - final_pos=env.init_qpos[:5], - alpha_phase=2, - policy=policy - ) - env.seed(seed + rank) - return env + _env = DmpEnvWrapper(_env, + num_dof=3, + num_basis=8, + duration=3.5, + alpha_phase=3, + dt=_env.dt, + learn_goal=False, + start_pos=_env.start_pos[1::2], + final_pos=_env.start_pos[1::2], + policy_type="motor" + # contextual=True + ) + _env.seed(seed + rank) + return _env return _init - dim = 25 - env = make_env(0, 0)() + dim = 24 + + n_samples = 10 + + vec_env = DmpAsyncVectorEnv([make_env(i) for i in range(4)], + n_samples=n_samples, + context="spawn", + shared_memory=False, + worker=_worker) + + params = 10 * np.random.randn(n_samples, dim) + + out = vec_env(params) + + non_vec_env = make_env(0, 0)() params = 10 * np.random.randn(dim) - out = env.rollout(params, render=True) + out2 = non_vec_env.rollout(params, render=True) - print(out) + print(out2) From 13a292f0e078df39280d9ff2d1a466f2e43f72eb Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 11 Feb 2021 12:32:32 +0100 Subject: [PATCH 08/43] updates --- alr_envs/classic_control/hole_reacher.py | 8 +- alr_envs/classic_control/utils.py | 2 +- alr_envs/mujoco/alr_mujoco_env.py | 13 ++- .../mujoco/ball_in_a_cup/ball_in_a_cup.py | 31 ++---- ...ward.py => ball_in_a_cup_reward_simple.py} | 11 ++- .../ball_in_a_cup/ball_in_a_cup_simple.py | 57 +++-------- alr_envs/utils/dmp_async_vec_env.py | 98 +++++++++---------- alr_envs/utils/dmp_env_wrapper.py | 11 +-- dmp_env_wrapper_example.py | 36 ++++--- dmp_pd_control_example.py | 4 +- 10 files changed, 116 insertions(+), 155 deletions(-) rename alr_envs/mujoco/ball_in_a_cup/{ball_in_a_cup_reward.py => ball_in_a_cup_reward_simple.py} (96%) diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index 0db772a..eeba84d 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -37,7 +37,7 @@ class HoleReacher(gym.Env): self.start_vel = np.zeros(self.num_links) self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer - self._dt = 0.01 + self.dt = 0.01 self.time_limit = 2 action_bound = np.pi * np.ones((self.num_links,)) @@ -82,9 +82,9 @@ class HoleReacher(gym.Env): a single step with an action in joint velocity space """ vel = action - acc = (vel - self._angle_velocity) / self._dt + acc = (vel - self._angle_velocity) / self.dt self._angle_velocity = vel - self._joint_angles = self._joint_angles + self._dt * self._angle_velocity + self._joint_angles = self._joint_angles + self.dt * self._angle_velocity self._update_joints() @@ -113,7 +113,7 @@ class HoleReacher(gym.Env): self._steps += 1 - done = self._steps * self._dt > self.time_limit or self._is_collided + done = self._steps * self.dt > self.time_limit or self._is_collided return self._get_obs().copy(), reward, done, info diff --git a/alr_envs/classic_control/utils.py b/alr_envs/classic_control/utils.py index 61156f1..b534eb9 100644 --- a/alr_envs/classic_control/utils.py +++ b/alr_envs/classic_control/utils.py @@ -26,7 +26,7 @@ def make_env(rank, seed=0): num_dof=5, num_basis=5, duration=2, - dt=env._dt, + dt=env.dt, learn_goal=True) env.seed(seed + rank) return env diff --git a/alr_envs/mujoco/alr_mujoco_env.py b/alr_envs/mujoco/alr_mujoco_env.py index c94beeb..c58dfe7 100644 --- a/alr_envs/mujoco/alr_mujoco_env.py +++ b/alr_envs/mujoco/alr_mujoco_env.py @@ -73,7 +73,7 @@ class AlrMujocoEnv(gym.Env): # observation, _reward, done, _info = self.step(action) # assert not done - observation = self.reset() + observation = self._get_obs() # TODO: is calling get_obs enough? should we call reset, or even step? self._set_observation_space(observation) @@ -82,14 +82,14 @@ class AlrMujocoEnv(gym.Env): @property def current_pos(self): """ - By default returns the joint positions of all simulated objects. May be overriden in subclass. + By default returns the joint positions of all simulated objects. May be overridden in subclass. """ return self.sim.data.qpos @property def current_vel(self): """ - By default returns the joint velocities of all simulated objects. May be overriden in subclass. + By default returns the joint velocities of all simulated objects. May be overridden in subclass. """ return self.sim.data.qvel @@ -125,10 +125,15 @@ class AlrMujocoEnv(gym.Env): # methods to override: # ---------------------------- + def _get_obs(self): + """Returns the observation. + """ + raise NotImplementedError() + def configure(self, *args, **kwargs): """ Helper method to set certain environment properties such as contexts in contextual environments since reset() - doesn't take arguments. Should be called before/after reset(). TODO: before or after? + doesn't take arguments. Should be called before reset(). """ pass diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py index 093e7f1..ad2e52a 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py @@ -1,12 +1,12 @@ -from gym.envs.mujoco import mujoco_env from gym import utils import os import numpy as np -from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward +from alr_envs.mujoco import alr_mujoco_env +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward_simple import BallInACupReward import mujoco_py -class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): +class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): def __init__(self, ): self._steps = 0 @@ -21,8 +21,12 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): self._q_pos = [] utils.EzPickle.__init__(self) - mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), - frame_skip=4) + alr_mujoco_env.AlrMujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), + n_substeps=4) + + def configure(self, context): + self.context = context + self.reward_function.reset(context) def reset_model(self): start_pos = self.init_qpos.copy() @@ -30,24 +34,8 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): start_vel = np.zeros_like(start_pos) self.set_state(start_pos, start_vel) self._steps = 0 - self.reward_function.reset() self._q_pos = [] - def do_simulation(self, ctrl, n_frames): - self.sim.data.ctrl[:] = ctrl - for _ in range(n_frames): - try: - self.sim.step() - except mujoco_py.builder.MujocoException as e: - # print("Error in simulation: " + str(e)) - # error = True - # Copy the current torque as if it would have been applied until the end of the trajectory - # for i in range(k + 1, sim_time): - # torques.append(trq) - return True - - return False - def step(self, a): # Apply gravity compensation if not np.all(self.sim.data.qfrc_applied[:7] == self.sim.data.qfrc_bias[:7]): @@ -98,6 +86,7 @@ class ALRBallInACupEnv(mujoco_env.MujocoEnv, utils.EzPickle): if __name__ == "__main__": env = ALRBallInACupEnv() + env.configure(None) env.reset() for i in range(2000): # objective.load_result("/tmp/cma") diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py similarity index 96% rename from alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py rename to alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py index 32310b3..22aa363 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py @@ -26,9 +26,9 @@ class BallInACupReward(alr_reward_fct.AlrReward): self.dists_final = None self.costs = None - self.reset() + self.reset(None) - def reset(self): + def reset(self, context): self.ball_traj = np.zeros(shape=(self.sim_time, 3)) self.dists = [] self.dists_final = [] @@ -51,11 +51,12 @@ class BallInACupReward(alr_reward_fct.AlrReward): self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) self.ball_traj[step, :] = ball_pos - if self.check_collision(sim): - return -1000, False, True - action_cost = np.sum(np.square(action)) + if self.check_collision(sim): + reward = - 1e-5 * action_cost - 1000 + return reward, False, True + if step == self.sim_time - 1: min_dist = np.min(self.dists) dist_final = self.dists_final[-1] diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py index 1a25211..efc702c 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py @@ -1,21 +1,16 @@ from alr_envs.mujoco import alr_mujoco_env -from gym import utils, spaces +from gym import utils import os import numpy as np -from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): - def __init__(self, reward_function=None): + def __init__(self, n_substeps=4, apply_gravity_comp=True, reward_function=None): self._steps = 0 self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "biac_base" + ".xml") - self.sim_time = 8 # seconds - self.sim_steps = int(self.sim_time / (0.0005 * 4)) # circular dependency.. sim.dt <-> mujocoenv init <-> reward fct - self.reward_function = reward_function(self.sim_steps) - self.start_pos = np.array([0.0, 0.58760536, 0.0, 1.36004913, 0.0, -0.32072943, -1.57]) self.start_vel = np.zeros(7) @@ -34,8 +29,15 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): utils.EzPickle.__init__(self) alr_mujoco_env.AlrMujocoEnv.__init__(self, self.xml_path, - apply_gravity_comp=True, - n_substeps=4) + apply_gravity_comp=apply_gravity_comp, + n_substeps=n_substeps) + + self.sim_time = 8 # seconds + self.sim_steps = int(self.sim_time / self.dt) + if reward_function is None: + from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward_simple import BallInACupReward + reward_function = BallInACupReward + self.reward_function = reward_function(self.sim_steps) @property def current_pos(self): @@ -47,6 +49,7 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): def configure(self, context): self.context = context + self.reward_function.reset(context) def reset_model(self): init_pos_all = self.init_qpos.copy() @@ -56,7 +59,6 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): goal_final_id = self.sim.model._site_name2id["cup_goal_final"] self._steps = 0 - self.reward_function.reset() self._q_pos = [] self._q_vel = [] @@ -65,38 +67,6 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): self.set_state(start_pos, init_vel) - # Reset the system - # self.sim.data.qpos[:] = init_pos_all - # self.sim.data.qvel[:] = init_vel - # self.sim.data.qpos[0:7] = init_pos_robot - # - # self.sim.step() - # - # self.sim.data.qpos[:] = init_pos_all - # self.sim.data.qvel[:] = init_vel - # self.sim.data.qpos[0:7] = init_pos_robot - # self.sim.data.body_xpos[ball_id, :] = np.copy(self.sim.data.site_xpos[goal_final_id, :]) - np.array([0., 0., 0.329]) - # - # # Stabilize the system around the initial position - # for i in range(0, 500): - # self.sim.data.qpos[7:] = 0. - # self.sim.data.qvel[7:] = 0. - # # self.sim.data.qpos[7] = -0.2 - # cur_pos = self.sim.data.qpos[0:7].copy() - # cur_vel = self.sim.data.qvel[0:7].copy() - # trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) - # self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() - # self.sim.step() - # self.render() - # - # for i in range(0, 500): - # cur_pos = self.sim.data.qpos[0:7].copy() - # cur_vel = self.sim.data.qvel[0:7].copy() - # trq = self.p_gains * (init_pos_robot - cur_pos) + self.d_gains * (np.zeros_like(init_pos_robot) - cur_vel) - # self.sim.data.qfrc_applied[0:7] = trq + self.sim.data.qfrc_bias[:7].copy() - # self.sim.step() - # self.render() - return self._get_obs() def step(self, a): @@ -154,7 +124,10 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): if __name__ == "__main__": + from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward_simple import BallInACupReward + env = ALRBallInACupEnv(reward_function=BallInACupReward) + env.configure(None) env.reset() env.render() for i in range(4000): diff --git a/alr_envs/utils/dmp_async_vec_env.py b/alr_envs/utils/dmp_async_vec_env.py index 771b0d8..641e770 100644 --- a/alr_envs/utils/dmp_async_vec_env.py +++ b/alr_envs/utils/dmp_async_vec_env.py @@ -7,9 +7,54 @@ import multiprocessing as mp import sys +def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): + assert shared_memory is None + env = env_fn() + parent_pipe.close() + try: + while True: + command, data = pipe.recv() + if command == 'reset': + observation = env.reset() + pipe.send((observation, True)) + elif command == 'step': + observation, reward, done, info = env.step(data) + if done: + observation = env.reset() + pipe.send(((observation, reward, done, info), True)) + elif command == 'rollout': + rewards = [] + infos = [] + for p, c in zip(*data): + reward, info = env.rollout(p, c) + rewards.append(reward) + infos.append(info) + pipe.send(((rewards, infos), (True, ) * len(rewards))) + elif command == 'seed': + env.seed(data) + pipe.send((None, True)) + elif command == 'close': + env.close() + pipe.send((None, True)) + break + elif command == 'idle': + pipe.send((None, True)) + elif command == '_check_observation_space': + pipe.send((data == env.observation_space, True)) + else: + raise RuntimeError('Received unknown command `{0}`. Must ' + 'be one of {`reset`, `step`, `seed`, `close`, ' + '`_check_observation_space`}.'.format(command)) + except (KeyboardInterrupt, Exception): + error_queue.put((index,) + sys.exc_info()[:2]) + pipe.send((None, False)) + finally: + env.close() + + class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): def __init__(self, env_fns, n_samples, observation_space=None, action_space=None, - shared_memory=True, copy=True, context=None, daemon=True, worker=None): + shared_memory=False, copy=True, context="spawn", daemon=True, worker=_worker): super(DmpAsyncVectorEnv, self).__init__(env_fns, observation_space=observation_space, action_space=action_space, @@ -91,7 +136,7 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): self._raise_if_errors(successes) self._state = AsyncState.DEFAULT - observations_list, rewards, dones, infos = [_flatten_list(r) for r in zip(*results)] + rewards, infos = [_flatten_list(r) for r in zip(*results)] # for now, we ignore the observations and only return the rewards @@ -109,55 +154,6 @@ class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): return self.rollout_wait() -def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): - assert shared_memory is None - env = env_fn() - parent_pipe.close() - try: - while True: - command, data = pipe.recv() - if command == 'reset': - observation = env.reset() - pipe.send((observation, True)) - elif command == 'step': - observation, reward, done, info = env.step(data) - if done: - observation = env.reset() - pipe.send(((observation, reward, done, info), True)) - elif command == 'rollout': - observations = [] - rewards = [] - dones = [] - infos = [] - for p, c in zip(*data): - observation, reward, done, info = env.rollout(p, c) - observations.append(observation) - rewards.append(reward) - dones.append(done) - infos.append(info) - pipe.send(((observations, rewards, dones, infos), (True, ) * len(rewards))) - elif command == 'seed': - env.seed(data) - pipe.send((None, True)) - elif command == 'close': - env.close() - pipe.send((None, True)) - break - elif command == 'idle': - pipe.send((None, True)) - elif command == '_check_observation_space': - pipe.send((data == env.observation_space, True)) - else: - raise RuntimeError('Received unknown command `{0}`. Must ' - 'be one of {`reset`, `step`, `seed`, `close`, ' - '`_check_observation_space`}.'.format(command)) - except (KeyboardInterrupt, Exception): - error_queue.put((index,) + sys.exc_info()[:2]) - pipe.send((None, False)) - finally: - env.close() - - def _flatten_obs(obs): assert isinstance(obs, (list, tuple)) assert len(obs) > 0 diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 3b461f7..3edc69f 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -69,15 +69,11 @@ class DmpEnvWrapper(gym.Wrapper): def __call__(self, params, contexts=None): params = np.atleast_2d(params) - observations = [] rewards = [] - dones = [] infos = [] for p, c in zip(params, contexts): - observation, reward, done, info = self.rollout(p, c) - observations.append(observation) + reward, info = self.rollout(p, c) rewards.append(reward) - dones.append(done) infos.append(info) return np.array(rewards), infos @@ -116,9 +112,8 @@ class DmpEnvWrapper(gym.Wrapper): rews = [] infos = [] + self.env.configure(context) self.env.reset() - if context is not None: - self.env.configure(context) for t, pos_vel in enumerate(zip(trajectory, velocity)): ac = self.policy.get_action(pos_vel[0], pos_vel[1]) @@ -132,4 +127,4 @@ class DmpEnvWrapper(gym.Wrapper): reward = np.sum(rews) - return obs, reward, done, info + return reward, info diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py index fcde324..6ed73e7 100644 --- a/dmp_env_wrapper_example.py +++ b/dmp_env_wrapper_example.py @@ -1,4 +1,4 @@ -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperVel +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker from alr_envs.classic_control.hole_reacher import HoleReacher import numpy as np @@ -16,21 +16,25 @@ if __name__ == "__main__": :param rank: (int) index of the subprocess """ def _init(): - env = HoleReacher(num_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.15, - hole_depth=1, - hole_x=1) + _env = HoleReacher(num_links=5, + allow_self_collision=False, + allow_wall_collision=False, + hole_width=0.15, + hole_depth=1, + hole_x=1) - env = DmpEnvWrapperVel(env, - num_dof=5, - num_basis=5, - duration=2, - dt=env._dt, - learn_goal=True) - env.seed(seed + rank) - return env + _env = DmpEnvWrapper(_env, + num_dof=5, + num_basis=5, + duration=2, + dt=_env.dt, + learn_goal=True, + alpha_phase=2, + start_pos=_env.start_pos, + policy_type="velocity" + ) + _env.seed(seed + rank) + return _env return _init n_samples = 4 @@ -45,6 +49,6 @@ if __name__ == "__main__": params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) # env.reset() - out = env.rollout(params) + out = env(params) print(out) diff --git a/dmp_pd_control_example.py b/dmp_pd_control_example.py index 9c4bec0..023080a 100644 --- a/dmp_pd_control_example.py +++ b/dmp_pd_control_example.py @@ -1,7 +1,6 @@ from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv -from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward import numpy as np @@ -17,7 +16,7 @@ if __name__ == "__main__": :param rank: (int) index of the subprocess """ def _init(): - _env = ALRBallInACupEnv(BallInACupReward) + _env = ALRBallInACupEnv() _env = DmpEnvWrapper(_env, num_dof=3, @@ -29,7 +28,6 @@ if __name__ == "__main__": start_pos=_env.start_pos[1::2], final_pos=_env.start_pos[1::2], policy_type="motor" - # contextual=True ) _env.seed(seed + rank) return _env From 708478c62676c64baa8edc78fe7570b3afbce6d6 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 11 Feb 2021 16:19:57 +0100 Subject: [PATCH 09/43] updates in biac --- .../mujoco/ball_in_a_cup/ball_in_a_cup.py | 35 +++++-- .../ball_in_a_cup/ball_in_a_cup_reward.py | 95 +++++++++++++++++++ .../ball_in_a_cup/ball_in_a_cup_simple.py | 3 +- alr_envs/mujoco/ball_in_a_cup/utils.py | 39 ++++++++ 4 files changed, 162 insertions(+), 10 deletions(-) create mode 100644 alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py create mode 100644 alr_envs/mujoco/ball_in_a_cup/utils.py diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py index ad2e52a..646c433 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py @@ -2,27 +2,44 @@ from gym import utils import os import numpy as np from alr_envs.mujoco import alr_mujoco_env -from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward_simple import BallInACupReward +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward import mujoco_py class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): - def __init__(self, ): + def __init__(self, n_substeps=4, apply_gravity_comp=True, reward_function=None): self._steps = 0 self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", - "ball-in-a-cup_base" + ".xml") - - self.sim_time = 8 # seconds - self.sim_steps = int(self.sim_time / (0.0005 * 4)) # circular dependency.. sim.dt <-> mujocoenv init <-> reward fct - self.reward_function = BallInACupReward(self.sim_steps) + "biac_base" + ".xml") self.start_pos = np.array([0.0, 0.58760536, 0.0, 1.36004913, 0.0, -0.32072943, -1.57]) + self.start_vel = np.zeros(7) + self._q_pos = [] + self._q_vel = [] + # self.weight_matrix_scale = 50 + self.max_ctrl = np.array([150., 125., 40., 60., 5., 5., 2.]) + self.p_gains = 1 / self.max_ctrl * np.array([200, 300, 100, 100, 10, 10, 2.5]) + self.d_gains = 1 / self.max_ctrl * np.array([7, 15, 5, 2.5, 0.3, 0.3, 0.05]) + + self.j_min = np.array([-2.6, -1.985, -2.8, -0.9, -4.55, -1.5707, -2.7]) + self.j_max = np.array([2.6, 1.985, 2.8, 3.14159, 1.25, 1.5707, 2.7]) + + self.context = None utils.EzPickle.__init__(self) - alr_mujoco_env.AlrMujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", "ball-in-a-cup_base.xml"), - n_substeps=4) + alr_mujoco_env.AlrMujocoEnv.__init__(self, + self.xml_path, + apply_gravity_comp=apply_gravity_comp, + n_substeps=n_substeps) + + self.sim_time = 8 # seconds + self.sim_steps = int(self.sim_time / self.dt) + if reward_function is None: + from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_reward import BallInACupReward + reward_function = BallInACupReward + self.reward_function = reward_function(self.sim_steps) def configure(self, context): self.context = context diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py new file mode 100644 index 0000000..76a4ec6 --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -0,0 +1,95 @@ +import numpy as np +from alr_envs.mujoco import alr_reward_fct + + +class BallInACupReward(alr_reward_fct.AlrReward): + def __init__(self, sim_time): + self.sim_time = sim_time + + self.collision_objects = ["cup_geom1", "cup_geom2", "wrist_palm_link_convex_geom", + "wrist_pitch_link_convex_decomposition_p1_geom", + "wrist_pitch_link_convex_decomposition_p2_geom", + "wrist_pitch_link_convex_decomposition_p3_geom", + "wrist_yaw_link_convex_decomposition_p1_geom", + "wrist_yaw_link_convex_decomposition_p2_geom", + "forearm_link_convex_decomposition_p1_geom", + "forearm_link_convex_decomposition_p2_geom"] + + self.ball_id = None + self.ball_collision_id = None + self.goal_id = None + self.goal_final_id = None + self.collision_ids = None + + self.ball_traj = None + self.dists = None + self.dists_final = None + self.costs = None + + self.reset(None) + + def reset(self, context): + self.ball_traj = np.zeros(shape=(self.sim_time, 3)) + self.dists = [] + self.dists_final = [] + self.costs = [] + self.context = context + + def compute_reward(self, action, sim, step, context=None): + self.ball_id = sim.model._body_name2id["ball"] + self.ball_collision_id = sim.model._geom_name2id["ball_geom"] + self.goal_id = sim.model._site_name2id["cup_goal"] + self.goal_final_id = sim.model._site_name2id["cup_goal_final"] + self.collision_ids = [sim.model._geom_name2id[name] for name in self.collision_objects] + + ball_in_cup = self.check_ball_in_cup(sim, self.ball_collision_id) + + # Compute the current distance from the ball to the inner part of the cup + goal_pos = sim.data.site_xpos[self.goal_id] + ball_pos = sim.data.body_xpos[self.ball_id] + goal_final_pos = sim.data.site_xpos[self.goal_final_id] + self.dists.append(np.linalg.norm(goal_pos - ball_pos)) + self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) + self.ball_traj[step, :] = ball_pos + + action_cost = np.sum(np.square(action)) + + if self.check_collision(sim): + reward = - 1e-5 * action_cost - 1000 + return reward, False, True + + if step == self.sim_time - 1: + min_dist = np.min(self.dists) + dist_final = self.dists_final[-1] + + cost = 0.5 * min_dist + 0.5 * dist_final + reward = np.exp(-2 * cost) - 1e-5 * action_cost + success = dist_final < 0.05 and ball_in_cup + else: + reward = - 1e-5 * action_cost + success = False + + return reward, success, False + + def check_ball_in_cup(self, sim, ball_collision_id): + cup_base_collision_id = sim.model._geom_name2id["cup_base_contact"] + for coni in range(0, sim.data.ncon): + con = sim.data.contact[coni] + + collision = con.geom1 == cup_base_collision_id and con.geom2 == ball_collision_id + collision_trans = con.geom1 == ball_collision_id and con.geom2 == cup_base_collision_id + + if collision or collision_trans: + return True + return False + + def check_collision(self, sim): + for coni in range(0, sim.data.ncon): + con = sim.data.contact[coni] + + collision = con.geom1 in self.collision_ids and con.geom2 == self.ball_collision_id + collision_trans = con.geom1 == self.ball_collision_id and con.geom2 in self.collision_ids + + if collision or collision_trans: + return True + return False diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py index efc702c..ab7e332 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py @@ -93,7 +93,8 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): velocity=angular_vel, # reward_balance=reward_balance, # end_effector=self.get_body_com("fingertip").copy(), goal=self.goal if hasattr(self, "goal") else None, - traj=self._q_pos) + traj=self._q_pos, + is_collided=crash or joint_cons_viol) def check_traj_in_joint_limits(self): return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min) diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py new file mode 100644 index 0000000..a4295da --- /dev/null +++ b/alr_envs/mujoco/ball_in_a_cup/utils.py @@ -0,0 +1,39 @@ +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv +from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv as ALRBallInACupEnvSimple + + +# TODO: add make_env for standard biac + + +def make_simple_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the initial seed for RNG + :param rank: (int) index of the subprocess + :returns a function that generates an environment + """ + + def _init(): + env = ALRBallInACupEnvSimple() + + env = DmpEnvWrapper(env, + policy_type="motor", + start_pos=env.start_pos[1::2], + final_pos=env.start_pos[1::2], + num_dof=3, + num_basis=8, + duration=3.5, + alpha_phase=3, + post_traj_time=4.5, + dt=env.dt, + learn_goal=False, + weights_scale=50) + + env.seed(seed + rank) + return env + + return _init From 95250af31cc24b7daddf5a85abff31bf517fa76d Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Fri, 12 Feb 2021 17:12:40 +0100 Subject: [PATCH 10/43] added viapoint reacher --- alr_envs/classic_control/viapoint_reacher.py | 313 +++++++++++++++++++ 1 file changed, 313 insertions(+) create mode 100644 alr_envs/classic_control/viapoint_reacher.py diff --git a/alr_envs/classic_control/viapoint_reacher.py b/alr_envs/classic_control/viapoint_reacher.py new file mode 100644 index 0000000..eeba84d --- /dev/null +++ b/alr_envs/classic_control/viapoint_reacher.py @@ -0,0 +1,313 @@ +import gym +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import patches + + +def ccw(A, B, C): + return (C[1]-A[1]) * (B[0]-A[0]) - (B[1]-A[1]) * (C[0]-A[0]) > 1e-12 + + +# Return true if line segments AB and CD intersect +def intersect(A, B, C, D): + return ccw(A, C, D) != ccw(B, C, D) and ccw(A, B, C) != ccw(A, B, D) + + +class HoleReacher(gym.Env): + + def __init__(self, num_links, hole_x, hole_width, hole_depth, allow_self_collision=False, + allow_wall_collision=False, collision_penalty=1000): + self.hole_x = hole_x # x-position of center of hole + self.hole_width = hole_width # width of hole + self.hole_depth = hole_depth # depth of hole + self.num_links = num_links + self.link_lengths = np.ones((num_links, 1)) + self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth]) + self.top_center_of_hole = np.hstack([hole_x, 0]) + self.left_wall_edge = np.hstack([hole_x - self.hole_width/2, 0]) + self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0]) + self.allow_self_collision = allow_self_collision + self.allow_wall_collision = allow_wall_collision + self.collision_penalty = collision_penalty + + self._joints = None + self._joint_angles = None + self._angle_velocity = None + self.start_pos = np.hstack([[np.pi/2], np.zeros(self.num_links - 1)]) + self.start_vel = np.zeros(self.num_links) + self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer + + self.dt = 0.01 + self.time_limit = 2 + + action_bound = np.pi * np.ones((self.num_links,)) + state_bound = np.hstack([ + [np.pi] * self.num_links, # cos + [np.pi] * self.num_links, # sin + [np.inf] * self.num_links, # velocity + [np.inf] * 2, # x-y coordinates of target distance + [np.inf] # env steps, because reward start after n steps TODO: Maybe + ]) + self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) + self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) + + self.fig = None + rect_1 = patches.Rectangle((-self.num_links, -1), + self.num_links + self.hole_x - self.hole_width / 2, 1, + fill=True, edgecolor='k', facecolor='k') + rect_2 = patches.Rectangle((self.hole_x + self.hole_width / 2, -1), + self.num_links - self.hole_x + self.hole_width / 2, 1, + fill=True, edgecolor='k', facecolor='k') + rect_3 = patches.Rectangle((self.hole_x - self.hole_width / 2, -1), self.hole_width, + 1 - self.hole_depth, + fill=True, edgecolor='k', facecolor='k') + + self.patches = [rect_1, rect_2, rect_3] + + @property + def end_effector(self): + return self._joints[self.num_links].T + + def reset(self): + self._joint_angles = self.start_pos + self._angle_velocity = self.start_vel + self._joints = np.zeros((self.num_links + 1, 2)) + self._update_joints() + self._steps = 0 + + return self._get_obs().copy() + + def step(self, action): + """ + a single step with an action in joint velocity space + """ + vel = action + acc = (vel - self._angle_velocity) / self.dt + self._angle_velocity = vel + self._joint_angles = self._joint_angles + self.dt * self._angle_velocity + + self._update_joints() + + # rew = self._reward() + + # compute reward directly in step function + + dist_reward = 0 + if not self._is_collided: + if self._steps == 180: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + else: + dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + + reward = - dist_reward ** 2 + + reward -= 1e-6 * np.sum(acc**2) + + if self._steps == 180: + reward -= 0.1 * np.sum(vel**2) ** 2 + + if self._is_collided: + reward -= self.collision_penalty + + info = {"is_collided": self._is_collided} + + self._steps += 1 + + done = self._steps * self.dt > self.time_limit or self._is_collided + + return self._get_obs().copy(), reward, done, info + + def _update_joints(self): + """ + update _joints to get new end effector position. The other links are only required for rendering. + Returns: + + """ + line_points_in_taskspace = self.get_forward_kinematics(num_points_per_link=20) + + self._joints[1:, 0] = self._joints[0, 0] + line_points_in_taskspace[:, -1, 0] + self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1] + + self_collision = False + wall_collision = False + + if not self.allow_self_collision: + self_collision = self.check_self_collision(line_points_in_taskspace) + if np.any(np.abs(self._joint_angles) > np.pi) and not self.allow_self_collision: + self_collision = True + + if not self.allow_wall_collision: + wall_collision = self.check_wall_collision(line_points_in_taskspace) + + self._is_collided = self_collision or wall_collision + + def _get_obs(self): + theta = self._joint_angles + return np.hstack([ + np.cos(theta), + np.sin(theta), + self._angle_velocity, + self.end_effector - self.bottom_center_of_hole, + self._steps + ]) + + # def _reward(self): + # dist_reward = 0 + # if not self._is_collided: + # if self._steps == 180: + # dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + # else: + # dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + # + # out = - dist_reward ** 2 + # + # return out + + def get_forward_kinematics(self, num_points_per_link=1): + theta = self._joint_angles[:, None] + + if num_points_per_link > 1: + intermediate_points = np.linspace(0, 1, num_points_per_link) + else: + intermediate_points = 1 + + accumulated_theta = np.cumsum(theta, axis=0) + + endeffector = np.zeros(shape=(self.num_links, num_points_per_link, 2)) + + x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points + y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points + + endeffector[0, :, 0] = x[0, :] + endeffector[0, :, 1] = y[0, :] + + for i in range(1, self.num_links): + endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0] + endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] + + return np.squeeze(endeffector + self._joints[0, :]) + + def check_self_collision(self, line_points): + for i, line1 in enumerate(line_points): + for line2 in line_points[i+2:, :, :]: + # if line1 != line2: + if intersect(line1[0], line1[-1], line2[0], line2[-1]): + return True + return False + + def check_wall_collision(self, line_points): + + # all points that are before the hole in x + r, c = np.where(line_points[:, :, 0] < (self.hole_x - self.hole_width / 2)) + + # check if any of those points are below surface + nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0) + + if nr_line_points_below_surface_before_hole > 0: + return True + + # all points that are after the hole in x + r, c = np.where(line_points[:, :, 0] > (self.hole_x + self.hole_width / 2)) + + # check if any of those points are below surface + nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0) + + if nr_line_points_below_surface_after_hole > 0: + return True + + # all points that are above the hole + r, c = np.where((line_points[:, :, 0] > (self.hole_x - self.hole_width / 2)) & ( + line_points[:, :, 0] < (self.hole_x + self.hole_width / 2))) + + # check if any of those points are below surface + nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self.hole_depth) + + if nr_line_points_below_surface_in_hole > 0: + return True + + return False + + def render(self, mode='human'): + if self.fig is None: + self.fig = plt.figure() + # plt.ion() + # plt.pause(0.01) + else: + plt.figure(self.fig.number) + + if mode == "human": + plt.cla() + plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}") + + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + + lim = np.sum(self.link_lengths) + 0.5 + plt.xlim([-lim, lim]) + plt.ylim([-1.1, lim]) + # plt.draw() + plt.pause(1e-4) # pushes window to foreground, which is annoying. + # self.fig.canvas.flush_events() + + elif mode == "partial": + if self._steps == 1: + # fig, ax = plt.subplots() + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + # plt.pause(0.01) + + if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided: + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', alpha=self._steps / 200) + # ax.plot(line_points_in_taskspace[:, 0, 0], + # line_points_in_taskspace[:, 0, 1], + # line_points_in_taskspace[:, -1, 0], + # line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200) + + lim = np.sum(self.link_lengths) + 0.5 + plt.xlim([-lim, lim]) + plt.ylim([-1.1, lim]) + plt.pause(0.01) + + elif mode == "final": + if self._steps == 199 or self._is_collided: + # fig, ax = plt.subplots() + + # Add the patch to the Axes + [plt.gca().add_patch(rect) for rect in self.patches] + + plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links) + # Arm + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + + plt.pause(0.01) + + def close(self): + if self.fig is not None: + plt.close(self.fig) + + +if __name__ == '__main__': + nl = 5 + render_mode = "human" # "human" or "partial" or "final" + env = HoleReacher(num_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15, hole_depth=1, hole_x=1) + env.reset() + # env.render(mode=render_mode) + + for i in range(200): + # objective.load_result("/tmp/cma") + # test with random actions + ac = 2 * env.action_space.sample() + # ac[0] += np.pi/2 + obs, rew, d, info = env.step(ac) + env.render(mode=render_mode) + + print(rew) + + if d: + break + + env.close() From 77d0cbd00ad9b319bd8d287ce16bd21ef37abd54 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Mon, 15 Feb 2021 09:03:19 +0100 Subject: [PATCH 11/43] updates --- alr_envs/classic_control/utils.py | 70 +++++++++++++++----- alr_envs/classic_control/viapoint_reacher.py | 67 +++++++------------ dmp_env_wrapper_example.py | 51 +++----------- dmp_pd_control_example.py | 57 +++++----------- 4 files changed, 103 insertions(+), 142 deletions(-) diff --git a/alr_envs/classic_control/utils.py b/alr_envs/classic_control/utils.py index b534eb9..f276d4a 100644 --- a/alr_envs/classic_control/utils.py +++ b/alr_envs/classic_control/utils.py @@ -1,8 +1,9 @@ from alr_envs.classic_control.hole_reacher import HoleReacher -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapperVel +from alr_envs.classic_control.viapoint_reacher import ViaPointReacher +from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper -def make_env(rank, seed=0): +def make_viapointreacher_env(rank, seed=0): """ Utility function for multiprocessed env. @@ -14,21 +15,56 @@ def make_env(rank, seed=0): """ def _init(): - env = HoleReacher(num_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.15, - hole_depth=1, - hole_x=1, - collision_penalty=100000) + _env = ViaPointReacher(num_links=5, + allow_self_collision=False, + collision_penalty=1000) - env = DmpEnvWrapperVel(env, - num_dof=5, - num_basis=5, - duration=2, - dt=env.dt, - learn_goal=True) - env.seed(seed + rank) - return env + _env = DmpEnvWrapper(_env, + num_dof=5, + num_basis=5, + duration=2, + alpha_phase=2, + dt=_env.dt, + start_pos=_env.start_pos, + learn_goal=False, + policy_type="velocity") + _env.seed(seed + rank) + return _env + + return _init + + +def make_holereacher_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the initial seed for RNG + :param rank: (int) index of the subprocess + :returns a function that generates an environment + """ + + def _init(): + _env = HoleReacher(num_links=5, + allow_self_collision=False, + allow_wall_collision=False, + hole_width=0.15, + hole_depth=1, + hole_x=1, + collision_penalty=100000) + + _env = DmpEnvWrapper(_env, + num_dof=5, + num_basis=5, + duration=2, + dt=_env.dt, + learn_goal=True, + alpha_phase=2, + start_pos=_env.start_pos, + policy_type="velocity" + ) + _env.seed(seed + rank) + return _env return _init diff --git a/alr_envs/classic_control/viapoint_reacher.py b/alr_envs/classic_control/viapoint_reacher.py index eeba84d..1cad10e 100644 --- a/alr_envs/classic_control/viapoint_reacher.py +++ b/alr_envs/classic_control/viapoint_reacher.py @@ -13,29 +13,24 @@ def intersect(A, B, C, D): return ccw(A, C, D) != ccw(B, C, D) and ccw(A, B, C) != ccw(A, B, D) -class HoleReacher(gym.Env): +class ViaPointReacher(gym.Env): - def __init__(self, num_links, hole_x, hole_width, hole_depth, allow_self_collision=False, - allow_wall_collision=False, collision_penalty=1000): - self.hole_x = hole_x # x-position of center of hole - self.hole_width = hole_width # width of hole - self.hole_depth = hole_depth # depth of hole + def __init__(self, num_links, allow_self_collision=False, + collision_penalty=1000): self.num_links = num_links self.link_lengths = np.ones((num_links, 1)) - self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth]) - self.top_center_of_hole = np.hstack([hole_x, 0]) - self.left_wall_edge = np.hstack([hole_x - self.hole_width/2, 0]) - self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0]) self.allow_self_collision = allow_self_collision - self.allow_wall_collision = allow_wall_collision self.collision_penalty = collision_penalty + self.via_point = np.ones(2) + self.goal_point = np.array((num_links, 0)) + self._joints = None self._joint_angles = None self._angle_velocity = None self.start_pos = np.hstack([[np.pi/2], np.zeros(self.num_links - 1)]) self.start_vel = np.zeros(self.num_links) - self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer + self.weight_matrix_scale = 1 self.dt = 0.01 self.time_limit = 2 @@ -52,22 +47,14 @@ class HoleReacher(gym.Env): self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) self.fig = None - rect_1 = patches.Rectangle((-self.num_links, -1), - self.num_links + self.hole_x - self.hole_width / 2, 1, - fill=True, edgecolor='k', facecolor='k') - rect_2 = patches.Rectangle((self.hole_x + self.hole_width / 2, -1), - self.num_links - self.hole_x + self.hole_width / 2, 1, - fill=True, edgecolor='k', facecolor='k') - rect_3 = patches.Rectangle((self.hole_x - self.hole_width / 2, -1), self.hole_width, - 1 - self.hole_depth, - fill=True, edgecolor='k', facecolor='k') - - self.patches = [rect_1, rect_2, rect_3] @property def end_effector(self): return self._joints[self.num_links].T + def configure(self, context): + pass + def reset(self): self._joint_angles = self.start_pos self._angle_velocity = self.start_vel @@ -94,16 +81,16 @@ class HoleReacher(gym.Env): dist_reward = 0 if not self._is_collided: - if self._steps == 180: - dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) - else: - dist_reward = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) + if self._steps == 100: + dist_reward = np.linalg.norm(self.end_effector - self.via_point) + if self._steps == 200: + dist_reward = np.linalg.norm(self.end_effector - self.goal_point) reward = - dist_reward ** 2 reward -= 1e-6 * np.sum(acc**2) - if self._steps == 180: + if self._steps == 200: reward -= 0.1 * np.sum(vel**2) ** 2 if self._is_collided: @@ -129,17 +116,13 @@ class HoleReacher(gym.Env): self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1] self_collision = False - wall_collision = False if not self.allow_self_collision: self_collision = self.check_self_collision(line_points_in_taskspace) if np.any(np.abs(self._joint_angles) > np.pi) and not self.allow_self_collision: self_collision = True - if not self.allow_wall_collision: - wall_collision = self.check_wall_collision(line_points_in_taskspace) - - self._is_collided = self_collision or wall_collision + self._is_collided = self_collision def _get_obs(self): theta = self._joint_angles @@ -147,7 +130,8 @@ class HoleReacher(gym.Env): np.cos(theta), np.sin(theta), self._angle_velocity, - self.end_effector - self.bottom_center_of_hole, + self.end_effector - self.via_point, + self.end_effector - self.goal_point, self._steps ]) @@ -237,17 +221,14 @@ class HoleReacher(gym.Env): if mode == "human": plt.cla() - plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}") + plt.title(f"Iteration: {self._steps}") # Arm plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') - # Add the patch to the Axes - [plt.gca().add_patch(rect) for rect in self.patches] - lim = np.sum(self.link_lengths) + 0.5 plt.xlim([-lim, lim]) - plt.ylim([-1.1, lim]) + plt.ylim([-lim, lim]) # plt.draw() plt.pause(1e-4) # pushes window to foreground, which is annoying. # self.fig.canvas.flush_events() @@ -293,14 +274,14 @@ class HoleReacher(gym.Env): if __name__ == '__main__': nl = 5 render_mode = "human" # "human" or "partial" or "final" - env = HoleReacher(num_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15, hole_depth=1, hole_x=1) + env = ViaPointReacher(num_links=nl, allow_self_collision=False) env.reset() - # env.render(mode=render_mode) + env.render(mode=render_mode) - for i in range(200): + for i in range(300): # objective.load_result("/tmp/cma") # test with random actions - ac = 2 * env.action_space.sample() + ac = env.action_space.sample() # ac[0] += np.pi/2 obs, rew, d, info = env.step(ac) env.render(mode=render_mode) diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py index 6ed73e7..e63e11c 100644 --- a/dmp_env_wrapper_example.py +++ b/dmp_env_wrapper_example.py @@ -1,52 +1,19 @@ -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper -from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker -from alr_envs.classic_control.hole_reacher import HoleReacher +from alr_envs.classic_control.utils import make_viapointreacher_env +from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv import numpy as np if __name__ == "__main__": - def make_env(rank, seed=0): - """ - Utility function for multiprocessed env. + n_samples = 10 + n_cpus = 4 + dim = 25 - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the inital seed for RNG - :param rank: (int) index of the subprocess - """ - def _init(): - _env = HoleReacher(num_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.15, - hole_depth=1, - hole_x=1) + env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)], + n_samples=n_samples) - _env = DmpEnvWrapper(_env, - num_dof=5, - num_basis=5, - duration=2, - dt=_env.dt, - learn_goal=True, - alpha_phase=2, - start_pos=_env.start_pos, - policy_type="velocity" - ) - _env.seed(seed + rank) - return _env - return _init - - n_samples = 4 - - env = DmpAsyncVectorEnv([make_env(i) for i in range(4)], - n_samples=n_samples, - context="spawn", - shared_memory=False, - worker=_worker) - - # params = np.random.randn(4, 25) - params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) + params = np.random.randn(n_samples, dim) + # params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) # env.reset() out = env(params) diff --git a/dmp_pd_control_example.py b/dmp_pd_control_example.py index 023080a..33abe6e 100644 --- a/dmp_pd_control_example.py +++ b/dmp_pd_control_example.py @@ -1,55 +1,32 @@ -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper -from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv, _worker -from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv +from alr_envs.mujoco.ball_in_a_cup.utils import make_simple_env +from alr_envs.utils.dmp_async_vec_env import DmpAsyncVectorEnv import numpy as np if __name__ == "__main__": - def make_env(rank, seed=0): - """ - Utility function for multiprocessed env. - - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the inital seed for RNG - :param rank: (int) index of the subprocess - """ - def _init(): - _env = ALRBallInACupEnv() - - _env = DmpEnvWrapper(_env, - num_dof=3, - num_basis=8, - duration=3.5, - alpha_phase=3, - dt=_env.dt, - learn_goal=False, - start_pos=_env.start_pos[1::2], - final_pos=_env.start_pos[1::2], - policy_type="motor" - ) - _env.seed(seed + rank) - return _env - return _init - dim = 24 + n_cpus = 4 n_samples = 10 - vec_env = DmpAsyncVectorEnv([make_env(i) for i in range(4)], - n_samples=n_samples, - context="spawn", - shared_memory=False, - worker=_worker) + vec_env = DmpAsyncVectorEnv([make_simple_env(i) for i in range(n_cpus)], + n_samples=n_samples) - params = 10 * np.random.randn(n_samples, dim) + # params = 10 * np.random.randn(n_samples, dim) + params = np.array([[ -4.51280364, 24.43701373, 15.73282129, -12.13020392, + -8.57305795, 2.79806606, -6.38613201, 5.99309385, + -2.05631886, 24.71684748, 14.05989949, -14.60456967, + 10.51933419, -2.43715355, -6.0767578 , 13.06498129, + 6.18038374, 11.4153859 , 1.40753639, 5.57082387, + 9.81989309, 3.60558787, -9.66996754, 14.28519904]]) out = vec_env(params) - - non_vec_env = make_env(0, 0)() - - params = 10 * np.random.randn(dim) + print(out) + # + non_vec_env = make_simple_env(0, 0)() + # + # params = 10 * np.random.randn(dim) out2 = non_vec_env.rollout(params, render=True) From 0916daf3b561460ad007028e049627df693417bb Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Mon, 15 Feb 2021 16:31:34 +0100 Subject: [PATCH 12/43] updates --- .../ball_in_a_cup_reward_simple.py | 6 +-- alr_envs/mujoco/ball_in_a_cup/utils.py | 26 +++++----- alr_envs/utils/detpmp_env_wrapper.py | 49 +++++++++---------- alr_envs/utils/dmp_env_wrapper.py | 13 +---- alr_envs/utils/policies.py | 9 ++++ 5 files changed, 49 insertions(+), 54 deletions(-) diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py index 22aa363..dcfd3d0 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward_simple.py @@ -54,7 +54,7 @@ class BallInACupReward(alr_reward_fct.AlrReward): action_cost = np.sum(np.square(action)) if self.check_collision(sim): - reward = - 1e-5 * action_cost - 1000 + reward = - 1e-4 * action_cost - 1000 return reward, False, True if step == self.sim_time - 1: @@ -62,10 +62,10 @@ class BallInACupReward(alr_reward_fct.AlrReward): dist_final = self.dists_final[-1] cost = 0.5 * min_dist + 0.5 * dist_final - reward = np.exp(-2 * cost) - 1e-5 * action_cost + reward = np.exp(-2 * cost) - 1e-4 * action_cost success = dist_final < 0.05 and ball_in_cup else: - reward = - 1e-5 * action_cost + reward = - 1e-4 * action_cost success = False return reward, success, False diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py index a4295da..27df36e 100644 --- a/alr_envs/mujoco/ball_in_a_cup/utils.py +++ b/alr_envs/mujoco/ball_in_a_cup/utils.py @@ -1,4 +1,4 @@ -from alr_envs.utils.dmp_env_wrapper import DmpEnvWrapper +from alr_envs.utils.detpmp_env_wrapper import DetPMPEnvWrapper from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv as ALRBallInACupEnvSimple @@ -20,18 +20,18 @@ def make_simple_env(rank, seed=0): def _init(): env = ALRBallInACupEnvSimple() - env = DmpEnvWrapper(env, - policy_type="motor", - start_pos=env.start_pos[1::2], - final_pos=env.start_pos[1::2], - num_dof=3, - num_basis=8, - duration=3.5, - alpha_phase=3, - post_traj_time=4.5, - dt=env.dt, - learn_goal=False, - weights_scale=50) + env = DetPMPEnvWrapper(env, + num_dof=3, + num_basis=5, + width=0.005, + policy_type="motor", + start_pos=env.start_pos[1::2], + duration=3.5, + post_traj_time=4.5, + dt=env.dt, + weights_scale=0.1, + zero_centered=True + ) env.seed(seed + rank) return env diff --git a/alr_envs/utils/detpmp_env_wrapper.py b/alr_envs/utils/detpmp_env_wrapper.py index 64986a5..8d80c0f 100644 --- a/alr_envs/utils/detpmp_env_wrapper.py +++ b/alr_envs/utils/detpmp_env_wrapper.py @@ -1,9 +1,10 @@ +from alr_envs.utils.policies import get_policy_class from mp_lib import det_promp import numpy as np import gym -class DetPMPEnvWrapperBase(gym.Wrapper): +class DetPMPEnvWrapper(gym.Wrapper): def __init__(self, env, num_dof, @@ -13,13 +14,15 @@ class DetPMPEnvWrapperBase(gym.Wrapper): duration=1, dt=0.01, post_traj_time=0., - policy=None, - weights_scale=1): - super(DetPMPEnvWrapperBase, self).__init__(env) + policy_type=None, + weights_scale=1, + zero_centered=False): + super(DetPMPEnvWrapper, self).__init__(env) self.num_dof = num_dof self.num_basis = num_basis self.dim = num_dof * num_basis - self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, width=width, off=0.01) + self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01, + zero_centered=zero_centered) weights = np.zeros(shape=(num_basis, num_dof)) self.pmp.set_weights(duration, weights) self.weights_scale = weights_scale @@ -29,52 +32,45 @@ class DetPMPEnvWrapperBase(gym.Wrapper): self.post_traj_steps = int(post_traj_time / dt) self.start_pos = start_pos + self.zero_centered = zero_centered - self.policy = policy + policy_class = get_policy_class(policy_type) + self.policy = policy_class(env) - def __call__(self, params): + def __call__(self, params, contexts=None): params = np.atleast_2d(params) - observations = [] rewards = [] - dones = [] infos = [] - for p in params: - observation, reward, done, info = self.rollout(p) - observations.append(observation) + for p, c in zip(params, contexts): + reward, info = self.rollout(p, c) rewards.append(reward) - dones.append(done) infos.append(info) return np.array(rewards), infos - def rollout(self, params, render=False): + def rollout(self, params, context=None, render=False): """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" - raise NotImplementedError - - -class DetPMPEnvWrapperPD(DetPMPEnvWrapperBase): - """ - Wrapper for gym environments which creates a trajectory in joint velocity space - """ - def rollout(self, params, render=False): params = np.reshape(params, newshape=(self.num_basis, self.num_dof)) * self.weights_scale self.pmp.set_weights(self.duration, params) - t, des_pos, des_vel, des_acc = self.pmp.compute_trajectory(1/self.dt, 1.) - des_pos += self.start_pos[None, :] + t, des_pos, des_vel, des_acc = self.pmp.compute_trajectory(1 / self.dt, 1.) + if self.zero_centered: + des_pos += self.start_pos[None, :] if self.post_traj_steps > 0: des_pos = np.vstack([des_pos, np.tile(des_pos[-1, :], [self.post_traj_steps, 1])]) des_vel = np.vstack([des_vel, np.zeros(shape=(self.post_traj_steps, self.num_dof))]) self._trajectory = des_pos + self._velocity = des_vel rews = [] infos = [] + self.env.configure(context) self.env.reset() for t, pos_vel in enumerate(zip(des_pos, des_vel)): - ac = self.policy.get_action(self.env, pos_vel[0], pos_vel[1]) + ac = self.policy.get_action(pos_vel[0], pos_vel[1]) obs, rew, done, info = self.env.step(ac) rews.append(rew) infos.append(info) @@ -85,4 +81,5 @@ class DetPMPEnvWrapperPD(DetPMPEnvWrapperBase): reward = np.sum(rews) - return obs, reward, done, info + return reward, info + diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 3edc69f..43bb030 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -1,3 +1,4 @@ +from alr_envs.utils.policies import get_policy_class from mp_lib.phase import ExpDecayPhaseGenerator from mp_lib.basis import DMPBasisGenerator from mp_lib import dmps @@ -5,18 +6,6 @@ import numpy as np import gym -def get_policy_class(policy_type): - if policy_type == "motor": - from alr_envs.utils.policies import PDController - return PDController - elif policy_type == "velocity": - from alr_envs.utils.policies import VelController - return VelController - elif policy_type == "position": - from alr_envs.utils.policies import PosController - return PosController - - class DmpEnvWrapper(gym.Wrapper): def __init__(self, env, diff --git a/alr_envs/utils/policies.py b/alr_envs/utils/policies.py index 0a4481c..e75beac 100644 --- a/alr_envs/utils/policies.py +++ b/alr_envs/utils/policies.py @@ -35,3 +35,12 @@ class PDController(BaseController): des_vel = self.env.extend_des_vel(des_vel) trq = self.p_gains * (des_pos - cur_pos) + self.d_gains * (des_vel - cur_vel) return trq + + +def get_policy_class(policy_type): + if policy_type == "motor": + return PDController + elif policy_type == "velocity": + return VelController + elif policy_type == "position": + return PosController From 7eef78d620835dfd21a9c044a89c137166437d54 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Tue, 16 Feb 2021 15:47:32 +0100 Subject: [PATCH 13/43] biac updates --- .../mujoco/ball_in_a_cup/assets/biac_base.xml | 2 +- .../mujoco/ball_in_a_cup/ball_in_a_cup.py | 71 +++++++++++-------- .../ball_in_a_cup/ball_in_a_cup_reward.py | 29 ++++++-- .../ball_in_a_cup/ball_in_a_cup_simple.py | 10 +-- alr_envs/mujoco/ball_in_a_cup/utils.py | 37 +++++++++- alr_envs/utils/detpmp_env_wrapper.py | 8 ++- 6 files changed, 110 insertions(+), 47 deletions(-) diff --git a/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml index c8c2643..490c853 100644 --- a/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml +++ b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml @@ -327,7 +327,7 @@ - + diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py index 646c433..e59c87d 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup.py @@ -41,54 +41,63 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): reward_function = BallInACupReward self.reward_function = reward_function(self.sim_steps) + @property + def current_pos(self): + return self.sim.data.qpos[0:7].copy() + + @property + def current_vel(self): + return self.sim.data.qvel[0:7].copy() + def configure(self, context): self.context = context self.reward_function.reset(context) def reset_model(self): - start_pos = self.init_qpos.copy() - start_pos[0:7] = self.start_pos - start_vel = np.zeros_like(start_pos) - self.set_state(start_pos, start_vel) + init_pos_all = self.init_qpos.copy() + init_pos_robot = self.start_pos + init_vel = np.zeros_like(init_pos_all) + self._steps = 0 self._q_pos = [] + self._q_vel = [] + + start_pos = init_pos_all + start_pos[0:7] = init_pos_robot + + self.set_state(start_pos, init_vel) + + return self._get_obs() def step(self, a): - # Apply gravity compensation - if not np.all(self.sim.data.qfrc_applied[:7] == self.sim.data.qfrc_bias[:7]): - self.sim.data.qfrc_applied[:7] = self.sim.data.qfrc_bias[:7] - reward_dist = 0.0 angular_vel = 0.0 - # if self._steps >= self.steps_before_reward: - # vec = self.get_body_com("fingertip") - self.get_body_com("target") - # reward_dist -= self.reward_weight * np.linalg.norm(vec) - # angular_vel -= np.linalg.norm(self.sim.data.qvel.flat[:self.n_links]) reward_ctrl = - np.square(a).sum() - # reward_balance = - self.balance_weight * np.abs( - # angle_normalize(np.sum(self.sim.data.qpos.flat[:self.n_links]), type="rad")) - # - # reward = reward_dist + reward_ctrl + angular_vel + reward_balance - # self.do_simulation(a, self.frame_skip) - crash = self.do_simulation(a, self.frame_skip) + crash = self.do_simulation(a) + joint_cons_viol = self.check_traj_in_joint_limits() self._q_pos.append(self.sim.data.qpos[0:7].ravel().copy()) + self._q_vel.append(self.sim.data.qvel[0:7].ravel().copy()) ob = self._get_obs() - if not crash: - reward, success, collision = self.reward_function.compute_reward(a, self.sim, self._steps) - done = success or self._steps == self.sim_steps - 1 or collision + if not crash and not joint_cons_viol: + reward, success, stop_sim = self.reward_function.compute_reward(a, self.sim, self._steps) + done = success or self._steps == self.sim_steps - 1 or stop_sim self._steps += 1 else: reward = -1000 + success = False done = True - return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl, - velocity=angular_vel, # reward_balance=reward_balance, - # end_effector=self.get_body_com("fingertip").copy(), - goal=self.goal if hasattr(self, "goal") else None, - traj=self._q_pos) + return ob, reward, done, dict(reward_dist=reward_dist, + reward_ctrl=reward_ctrl, + velocity=angular_vel, + traj=self._q_pos, is_success=success, + is_collided=crash or joint_cons_viol) + + def check_traj_in_joint_limits(self): + return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min) def _get_obs(self): theta = self.sim.data.qpos.flat[:7] @@ -103,13 +112,15 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): if __name__ == "__main__": env = ALRBallInACupEnv() - env.configure(None) + ctxt = np.array([-0.20869846, -0.66376693, 1.18088501]) + + env.configure(ctxt) env.reset() + env.render() for i in range(2000): - # objective.load_result("/tmp/cma") # test with random actions - # ac = 0.0 * env.action_space.sample() - ac = env.start_pos + ac = 0.01 * env.action_space.sample()[0:7] + # ac = env.start_pos # ac[0] += np.pi/2 obs, rew, d, info = env.step(ac) env.render() diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index 76a4ec6..525fe34 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -23,6 +23,7 @@ class BallInACupReward(alr_reward_fct.AlrReward): self.ball_traj = None self.dists = None + self.dists_ctxt = None self.dists_final = None self.costs = None @@ -31,11 +32,12 @@ class BallInACupReward(alr_reward_fct.AlrReward): def reset(self, context): self.ball_traj = np.zeros(shape=(self.sim_time, 3)) self.dists = [] + self.dists_ctxt = [] self.dists_final = [] self.costs = [] self.context = context - def compute_reward(self, action, sim, step, context=None): + def compute_reward(self, action, sim, step): self.ball_id = sim.model._body_name2id["ball"] self.ball_collision_id = sim.model._geom_name2id["ball_geom"] self.goal_id = sim.model._site_name2id["cup_goal"] @@ -49,27 +51,42 @@ class BallInACupReward(alr_reward_fct.AlrReward): ball_pos = sim.data.body_xpos[self.ball_id] goal_final_pos = sim.data.site_xpos[self.goal_final_id] self.dists.append(np.linalg.norm(goal_pos - ball_pos)) + self.dists_ctxt.append(np.linalg.norm(ball_pos - self.context)) self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) self.ball_traj[step, :] = ball_pos action_cost = np.sum(np.square(action)) + stop_sim = False + success = False + if self.check_collision(sim): reward = - 1e-5 * action_cost - 1000 - return reward, False, True + stop_sim = True + return reward, success, stop_sim - if step == self.sim_time - 1: + if ball_in_cup or step == self.sim_time - 1: min_dist = np.min(self.dists) dist_final = self.dists_final[-1] + dist_ctxt = self.dists_ctxt[-1] - cost = 0.5 * min_dist + 0.5 * dist_final - reward = np.exp(-2 * cost) - 1e-5 * action_cost + cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) + reward = np.exp(-1 * cost) - 1e-5 * action_cost + stop_sim = True success = dist_final < 0.05 and ball_in_cup else: reward = - 1e-5 * action_cost success = False - return reward, success, False + return reward, success, stop_sim + + def _get_stage_wise_cost(self, ball_in_cup, min_dist, dist_final, dist_to_ctxt): + if not ball_in_cup: + cost = 3 + 2*(0.5 * min_dist**2 + 0.5 * dist_final**2) + else: + cost = 2 * dist_to_ctxt ** 2 + print('Context Distance:', dist_to_ctxt) + return cost def check_ball_in_cup(self, sim, ball_collision_id): cup_base_collision_id = sim.model._geom_name2id["cup_base_contact"] diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py index ab7e332..2356d9e 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_simple.py @@ -88,12 +88,12 @@ class ALRBallInACupEnv(alr_mujoco_env.AlrMujocoEnv, utils.EzPickle): self._steps += 1 else: reward = -1000 + success = False done = True - return ob, reward, done, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl, - velocity=angular_vel, # reward_balance=reward_balance, - # end_effector=self.get_body_com("fingertip").copy(), - goal=self.goal if hasattr(self, "goal") else None, - traj=self._q_pos, + return ob, reward, done, dict(reward_dist=reward_dist, + reward_ctrl=reward_ctrl, + velocity=angular_vel, + traj=self._q_pos, is_success=success, is_collided=crash or joint_cons_viol) def check_traj_in_joint_limits(self): diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py index 27df36e..8e94670 100644 --- a/alr_envs/mujoco/ball_in_a_cup/utils.py +++ b/alr_envs/mujoco/ball_in_a_cup/utils.py @@ -3,7 +3,39 @@ from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup_simple import ALRBallInACupEnv as ALRBallInACupEnvSimple -# TODO: add make_env for standard biac +def make_env(rank, seed=0): + """ + Utility function for multiprocessed env. + + :param env_id: (str) the environment ID + :param num_env: (int) the number of environments you wish to have in subprocesses + :param seed: (int) the initial seed for RNG + :param rank: (int) index of the subprocess + :returns a function that generates an environment + """ + + def _init(): + env = ALRBallInACupEnv() + + env = DetPMPEnvWrapper(env, + num_dof=7, + num_basis=5, + width=0.005, + policy_type="motor", + start_pos=env.start_pos, + duration=3.5, + post_traj_time=4.5, + dt=env.dt, + weights_scale=0.1, + zero_start=True, + zero_goal=False + ) + + env.seed(seed + rank) + return env + + return _init + def make_simple_env(rank, seed=0): @@ -30,7 +62,8 @@ def make_simple_env(rank, seed=0): post_traj_time=4.5, dt=env.dt, weights_scale=0.1, - zero_centered=True + zero_start=True, + zero_goal=True ) env.seed(seed + rank) diff --git a/alr_envs/utils/detpmp_env_wrapper.py b/alr_envs/utils/detpmp_env_wrapper.py index 8d80c0f..3e72c06 100644 --- a/alr_envs/utils/detpmp_env_wrapper.py +++ b/alr_envs/utils/detpmp_env_wrapper.py @@ -16,13 +16,15 @@ class DetPMPEnvWrapper(gym.Wrapper): post_traj_time=0., policy_type=None, weights_scale=1, - zero_centered=False): + zero_start=False, + zero_goal=False, + ): super(DetPMPEnvWrapper, self).__init__(env) self.num_dof = num_dof self.num_basis = num_basis self.dim = num_dof * num_basis self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01, - zero_centered=zero_centered) + zero_start=zero_start, zero_goal=zero_goal) weights = np.zeros(shape=(num_basis, num_dof)) self.pmp.set_weights(duration, weights) self.weights_scale = weights_scale @@ -32,7 +34,7 @@ class DetPMPEnvWrapper(gym.Wrapper): self.post_traj_steps = int(post_traj_time / dt) self.start_pos = start_pos - self.zero_centered = zero_centered + self.zero_centered = zero_start policy_class = get_policy_class(policy_type) self.policy = policy_class(env) From 420fe105068f54ce018cc923586bed30c15263cc Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Tue, 16 Feb 2021 18:47:08 +0100 Subject: [PATCH 14/43] biac normal cost --- alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index 525fe34..7c68e35 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -61,7 +61,7 @@ class BallInACupReward(alr_reward_fct.AlrReward): success = False if self.check_collision(sim): - reward = - 1e-5 * action_cost - 1000 + reward = - 1e-4 * action_cost - 1000 stop_sim = True return reward, success, stop_sim @@ -70,12 +70,13 @@ class BallInACupReward(alr_reward_fct.AlrReward): dist_final = self.dists_final[-1] dist_ctxt = self.dists_ctxt[-1] - cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) - reward = np.exp(-1 * cost) - 1e-5 * action_cost + # cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) + cost = 2 * (0.33 * min_dist + 0.33 * dist_final + 0.33 * dist_ctxt) + reward = np.exp(-1 * cost) - 1e-4 * action_cost stop_sim = True success = dist_final < 0.05 and ball_in_cup else: - reward = - 1e-5 * action_cost + reward = - 1e-4 * action_cost success = False return reward, success, stop_sim From 46fc642c36c5a294f61e91943eb60b47fd74f339 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Wed, 17 Feb 2021 17:48:05 +0100 Subject: [PATCH 15/43] updates --- alr_envs/classic_control/hole_reacher.py | 4 +++- alr_envs/classic_control/utils.py | 3 ++- .../ball_in_a_cup/ball_in_a_cup_reward.py | 4 ++-- alr_envs/utils/dmp_env_wrapper.py | 2 -- dmp_env_wrapper_example.py | 14 +++++++----- dmp_pd_control_example.py | 22 +++++++------------ 6 files changed, 23 insertions(+), 26 deletions(-) diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index eeba84d..be1cd1f 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -35,7 +35,6 @@ class HoleReacher(gym.Env): self._angle_velocity = None self.start_pos = np.hstack([[np.pi/2], np.zeros(self.num_links - 1)]) self.start_vel = np.zeros(self.num_links) - self.weight_matrix_scale = 50 # for the holereacher, the dmp weights become quite large compared to the values of the goal attractor. this scaling is to ensure they are on similar scale for the optimizer self.dt = 0.01 self.time_limit = 2 @@ -68,6 +67,9 @@ class HoleReacher(gym.Env): def end_effector(self): return self._joints[self.num_links].T + def configure(self, context): + pass + def reset(self): self._joint_angles = self.start_pos self._angle_velocity = self.start_vel diff --git a/alr_envs/classic_control/utils.py b/alr_envs/classic_control/utils.py index f276d4a..9da138f 100644 --- a/alr_envs/classic_control/utils.py +++ b/alr_envs/classic_control/utils.py @@ -62,7 +62,8 @@ def make_holereacher_env(rank, seed=0): learn_goal=True, alpha_phase=2, start_pos=_env.start_pos, - policy_type="velocity" + policy_type="velocity", + weights_scale=100, ) _env.seed(seed + rank) return _env diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index 7c68e35..73dc1c5 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -71,10 +71,10 @@ class BallInACupReward(alr_reward_fct.AlrReward): dist_ctxt = self.dists_ctxt[-1] # cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) - cost = 2 * (0.33 * min_dist + 0.33 * dist_final + 0.33 * dist_ctxt) + cost = 2 * (0.5 * min_dist + 0.5 * dist_final + 0.1 * dist_ctxt) reward = np.exp(-1 * cost) - 1e-4 * action_cost stop_sim = True - success = dist_final < 0.05 and ball_in_cup + success = dist_final < 0.05 and dist_ctxt < 0.05 else: reward = - 1e-4 * action_cost success = False diff --git a/alr_envs/utils/dmp_env_wrapper.py b/alr_envs/utils/dmp_env_wrapper.py index 43bb030..849ac1b 100644 --- a/alr_envs/utils/dmp_env_wrapper.py +++ b/alr_envs/utils/dmp_env_wrapper.py @@ -86,8 +86,6 @@ class DmpEnvWrapper(gym.Wrapper): def rollout(self, params, context=None, render=False): """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" goal_pos, weight_matrix = self.goal_and_weights(params) - if hasattr(self.env, "weight_matrix_scale"): - weight_matrix = weight_matrix * self.env.weight_matrix_scale self.dmp.set_weights(weight_matrix, goal_pos) trajectory, velocity = self.dmp.reference_trajectory(self.t) diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py index e63e11c..b971574 100644 --- a/dmp_env_wrapper_example.py +++ b/dmp_env_wrapper_example.py @@ -5,17 +5,19 @@ import numpy as np if __name__ == "__main__": - n_samples = 10 + n_samples = 1 n_cpus = 4 dim = 25 - env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)], - n_samples=n_samples) + # env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)], + # n_samples=n_samples) + + test_env = make_viapointreacher_env(0)() params = np.random.randn(n_samples, dim) # params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) - # env.reset() - out = env(params) + test_env.rollout(params, render=True) - print(out) + # out = env(params) + # print(out) diff --git a/dmp_pd_control_example.py b/dmp_pd_control_example.py index 33abe6e..303f979 100644 --- a/dmp_pd_control_example.py +++ b/dmp_pd_control_example.py @@ -5,29 +5,23 @@ import numpy as np if __name__ == "__main__": - dim = 24 + dim = 15 n_cpus = 4 - n_samples = 10 + n_samples = 1 vec_env = DmpAsyncVectorEnv([make_simple_env(i) for i in range(n_cpus)], n_samples=n_samples) - # params = 10 * np.random.randn(n_samples, dim) - params = np.array([[ -4.51280364, 24.43701373, 15.73282129, -12.13020392, - -8.57305795, 2.79806606, -6.38613201, 5.99309385, - -2.05631886, 24.71684748, 14.05989949, -14.60456967, - 10.51933419, -2.43715355, -6.0767578 , 13.06498129, - 6.18038374, 11.4153859 , 1.40753639, 5.57082387, - 9.81989309, 3.60558787, -9.66996754, 14.28519904]]) + params = np.tile(1 * np.random.randn(n_samples, dim), (10, 1)) - out = vec_env(params) - print(out) + rewards, infos = vec_env(params) + print(rewards) # - non_vec_env = make_simple_env(0, 0)() + # non_vec_env = make_simple_env(0, 0)() # # params = 10 * np.random.randn(dim) - out2 = non_vec_env.rollout(params, render=True) + # out2 = non_vec_env.rollout(params, render=True) - print(out2) + # print(out2) From 7ed22df77806571ec59864cfd87e5ffd97ef3606 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Wed, 17 Feb 2021 18:50:55 +0100 Subject: [PATCH 16/43] update biac reward --- alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py | 1 - alr_envs/mujoco/ball_in_a_cup/utils.py | 3 +-- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index 73dc1c5..bb7440d 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -73,7 +73,6 @@ class BallInACupReward(alr_reward_fct.AlrReward): # cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) cost = 2 * (0.5 * min_dist + 0.5 * dist_final + 0.1 * dist_ctxt) reward = np.exp(-1 * cost) - 1e-4 * action_cost - stop_sim = True success = dist_final < 0.05 and dist_ctxt < 0.05 else: reward = - 1e-4 * action_cost diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py index 8e94670..d2b57f9 100644 --- a/alr_envs/mujoco/ball_in_a_cup/utils.py +++ b/alr_envs/mujoco/ball_in_a_cup/utils.py @@ -28,7 +28,7 @@ def make_env(rank, seed=0): dt=env.dt, weights_scale=0.1, zero_start=True, - zero_goal=False + zero_goal=True ) env.seed(seed + rank) @@ -37,7 +37,6 @@ def make_env(rank, seed=0): return _init - def make_simple_env(rank, seed=0): """ Utility function for multiprocessed env. From dd18a04df6003d7107477e2f72ffd1590d09110e Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Thu, 18 Feb 2021 11:33:55 +0100 Subject: [PATCH 17/43] biac reward function update --- .../ball_in_a_cup/ball_in_a_cup_reward.py | 38 +++++++++++-------- 1 file changed, 23 insertions(+), 15 deletions(-) diff --git a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py index bb7440d..2465b09 100644 --- a/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py +++ b/alr_envs/mujoco/ball_in_a_cup/ball_in_a_cup_reward.py @@ -36,44 +36,52 @@ class BallInACupReward(alr_reward_fct.AlrReward): self.dists_final = [] self.costs = [] self.context = context + self.ball_in_cup = False + self.dist_ctxt = 5 def compute_reward(self, action, sim, step): + action_cost = np.sum(np.square(action)) + + stop_sim = False + success = False + self.ball_id = sim.model._body_name2id["ball"] self.ball_collision_id = sim.model._geom_name2id["ball_geom"] self.goal_id = sim.model._site_name2id["cup_goal"] self.goal_final_id = sim.model._site_name2id["cup_goal_final"] self.collision_ids = [sim.model._geom_name2id[name] for name in self.collision_objects] - ball_in_cup = self.check_ball_in_cup(sim, self.ball_collision_id) + if self.check_collision(sim): + reward = - 1e-4 * action_cost - 1000 + stop_sim = True + return reward, success, stop_sim # Compute the current distance from the ball to the inner part of the cup goal_pos = sim.data.site_xpos[self.goal_id] ball_pos = sim.data.body_xpos[self.ball_id] goal_final_pos = sim.data.site_xpos[self.goal_final_id] self.dists.append(np.linalg.norm(goal_pos - ball_pos)) - self.dists_ctxt.append(np.linalg.norm(ball_pos - self.context)) self.dists_final.append(np.linalg.norm(goal_final_pos - ball_pos)) + self.dists_ctxt.append(np.linalg.norm(ball_pos - self.context)) self.ball_traj[step, :] = ball_pos - action_cost = np.sum(np.square(action)) + # Determine the first time when ball is in cup + if not self.ball_in_cup: + ball_in_cup = self.check_ball_in_cup(sim, self.ball_collision_id) + self.ball_in_cup = ball_in_cup + if ball_in_cup: + dist_to_ctxt = np.linalg.norm(ball_pos - self.context) + self.dist_ctxt = dist_to_ctxt - stop_sim = False - success = False - - if self.check_collision(sim): - reward = - 1e-4 * action_cost - 1000 - stop_sim = True - return reward, success, stop_sim - - if ball_in_cup or step == self.sim_time - 1: + if step == self.sim_time - 1: min_dist = np.min(self.dists) dist_final = self.dists_final[-1] - dist_ctxt = self.dists_ctxt[-1] + # dist_ctxt = self.dists_ctxt[-1] # cost = self._get_stage_wise_cost(ball_in_cup, min_dist, dist_final, dist_ctxt) - cost = 2 * (0.5 * min_dist + 0.5 * dist_final + 0.1 * dist_ctxt) + cost = 2 * (0.5 * min_dist + 0.5 * dist_final + 0.1 * self.dist_ctxt) reward = np.exp(-1 * cost) - 1e-4 * action_cost - success = dist_final < 0.05 and dist_ctxt < 0.05 + success = dist_final < 0.05 and self.dist_ctxt < 0.05 else: reward = - 1e-4 * action_cost success = False From 1e3f0364786f4ed30dc3b5e4ddbb76630cd1af82 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Fri, 19 Feb 2021 10:00:26 +0100 Subject: [PATCH 18/43] updates --- alr_envs/classic_control/simple_reacher.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/alr_envs/classic_control/simple_reacher.py b/alr_envs/classic_control/simple_reacher.py index c7e6ddb..f547ad6 100644 --- a/alr_envs/classic_control/simple_reacher.py +++ b/alr_envs/classic_control/simple_reacher.py @@ -9,8 +9,8 @@ from gym.utils import seeding from alr_envs.utils.utils import angle_normalize -if os.environ.get("DISPLAY", None): - mpl.use('Qt5Agg') +# if os.environ.get("DISPLAY", None): +# mpl.use('Qt5Agg') class SimpleReacherEnv(gym.Env): From 60e1673ee164771b106e4f4fe2b8d12f998e039d Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Fri, 19 Feb 2021 16:17:55 +0100 Subject: [PATCH 19/43] viapoint reacher reward bug fix --- alr_envs/classic_control/utils.py | 5 +++-- alr_envs/classic_control/viapoint_reacher.py | 2 +- dmp_env_wrapper_example.py | 13 +++++++++++-- 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/alr_envs/classic_control/utils.py b/alr_envs/classic_control/utils.py index 9da138f..f2ead72 100644 --- a/alr_envs/classic_control/utils.py +++ b/alr_envs/classic_control/utils.py @@ -23,11 +23,12 @@ def make_viapointreacher_env(rank, seed=0): num_dof=5, num_basis=5, duration=2, - alpha_phase=2, + alpha_phase=2.5, dt=_env.dt, start_pos=_env.start_pos, learn_goal=False, - policy_type="velocity") + policy_type="velocity", + weights_scale=50) _env.seed(seed + rank) return _env diff --git a/alr_envs/classic_control/viapoint_reacher.py b/alr_envs/classic_control/viapoint_reacher.py index 1cad10e..f6a3474 100644 --- a/alr_envs/classic_control/viapoint_reacher.py +++ b/alr_envs/classic_control/viapoint_reacher.py @@ -83,7 +83,7 @@ class ViaPointReacher(gym.Env): if not self._is_collided: if self._steps == 100: dist_reward = np.linalg.norm(self.end_effector - self.via_point) - if self._steps == 200: + if self._steps == 199: dist_reward = np.linalg.norm(self.end_effector - self.goal_point) reward = - dist_reward ** 2 diff --git a/dmp_env_wrapper_example.py b/dmp_env_wrapper_example.py index b971574..6fd0a57 100644 --- a/dmp_env_wrapper_example.py +++ b/dmp_env_wrapper_example.py @@ -14,10 +14,19 @@ if __name__ == "__main__": test_env = make_viapointreacher_env(0)() - params = np.random.randn(n_samples, dim) + # params = np.random.randn(n_samples, dim) + params = np.array([ 217.54494933, -1.85169983, 24.08414447, 42.23816868, + 23.32071702, 7.60780651, -31.74777741, 265.50634253, + 463.43822562, 245.93948374, -272.64003621, -45.24999553, + 503.21185823, 809.17742517, 393.12387021, -196.54196471, + 6.79327307, 374.82429078, 552.4119579 , 197.3963343 , + 243.87357056, -39.56041541, -616.93957463, -710.0772516 , + -414.21769789]) + # params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) - test_env.rollout(params, render=True) + rew, info = test_env.rollout(params, render=True) + print(rew) # out = env(params) # print(out) From 7e988758fe5ab90848521a4be2950ba8d7012ad2 Mon Sep 17 00:00:00 2001 From: Maximilian Huettenrauch Date: Wed, 24 Feb 2021 15:37:54 +0100 Subject: [PATCH 20/43] updates --- .../mujoco/ball_in_a_cup/assets/biac_base.xml | 4 +- .../mujoco/ball_in_a_cup/ball_in_a_cup.py | 10 +- .../ball_in_a_cup/ball_in_a_cup_reward.py | 16 +- alr_envs/mujoco/ball_in_a_cup/utils.py | 40 +++- alr_envs/mujoco/beerpong/__init__.py | 0 alr_envs/mujoco/beerpong/assets/beerpong.xml | 226 ++++++++++++++++++ alr_envs/mujoco/beerpong/beerpong.py | 143 +++++++++++ alr_envs/mujoco/beerpong/beerpong_reward.py | 124 ++++++++++ .../wam}/base_link_convex.stl | Bin .../meshes => meshes/wam}/base_link_fine.stl | Bin .../assets/meshes => meshes/wam}/cup.stl | Bin .../meshes => meshes/wam}/cup_split1.stl | Bin .../meshes => meshes/wam}/cup_split10.stl | Bin .../meshes => meshes/wam}/cup_split11.stl | Bin .../meshes => meshes/wam}/cup_split12.stl | Bin .../meshes => meshes/wam}/cup_split13.stl | Bin .../meshes => meshes/wam}/cup_split14.stl | Bin .../meshes => meshes/wam}/cup_split15.stl | Bin .../meshes => meshes/wam}/cup_split16.stl | Bin .../meshes => meshes/wam}/cup_split17.stl | Bin .../meshes => meshes/wam}/cup_split18.stl | Bin .../meshes => meshes/wam}/cup_split2.stl | Bin .../meshes => meshes/wam}/cup_split3.stl | Bin .../meshes => meshes/wam}/cup_split4.stl | Bin .../meshes => meshes/wam}/cup_split5.stl | Bin .../meshes => meshes/wam}/cup_split6.stl | Bin .../meshes => meshes/wam}/cup_split7.stl | Bin .../meshes => meshes/wam}/cup_split8.stl | Bin .../meshes => meshes/wam}/cup_split9.stl | Bin .../wam}/elbow_link_convex.stl | Bin .../meshes => meshes/wam}/elbow_link_fine.stl | Bin .../forearm_link_convex_decomposition_p1.stl | Bin .../forearm_link_convex_decomposition_p2.stl | Bin .../wam}/forearm_link_fine.stl | Bin .../shoulder_link_convex_decomposition_p1.stl | Bin .../shoulder_link_convex_decomposition_p2.stl | Bin .../shoulder_link_convex_decomposition_p3.stl | Bin .../wam}/shoulder_link_fine.stl | Bin .../wam}/shoulder_pitch_link_convex.stl | Bin .../wam}/shoulder_pitch_link_fine.stl | Bin ...upper_arm_link_convex_decomposition_p1.stl | Bin ...upper_arm_link_convex_decomposition_p2.stl | Bin .../wam}/upper_arm_link_fine.stl | Bin .../wam}/wrist_palm_link_convex.stl | Bin .../wam}/wrist_palm_link_fine.stl | Bin ...ist_pitch_link_convex_decomposition_p1.stl | Bin ...ist_pitch_link_convex_decomposition_p2.stl | Bin ...ist_pitch_link_convex_decomposition_p3.stl | Bin .../wam}/wrist_pitch_link_fine.stl | Bin ...wrist_yaw_link_convex_decomposition_p1.stl | Bin ...wrist_yaw_link_convex_decomposition_p2.stl | Bin .../wam}/wrist_yaw_link_fine.stl | Bin 52 files changed, 550 insertions(+), 13 deletions(-) create mode 100644 alr_envs/mujoco/beerpong/__init__.py create mode 100644 alr_envs/mujoco/beerpong/assets/beerpong.xml create mode 100644 alr_envs/mujoco/beerpong/beerpong.py create mode 100644 alr_envs/mujoco/beerpong/beerpong_reward.py rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/base_link_convex.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/base_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split10.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split11.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split12.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split13.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split14.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split15.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split16.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split17.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split18.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split3.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split4.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split5.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split6.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split7.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split8.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/cup_split9.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/elbow_link_convex.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/elbow_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/forearm_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/forearm_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/forearm_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_link_convex_decomposition_p3.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_pitch_link_convex.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/shoulder_pitch_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/upper_arm_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/upper_arm_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/upper_arm_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_palm_link_convex.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_palm_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_pitch_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_pitch_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_pitch_link_convex_decomposition_p3.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_pitch_link_fine.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_yaw_link_convex_decomposition_p1.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_yaw_link_convex_decomposition_p2.stl (100%) rename alr_envs/mujoco/{ball_in_a_cup/assets/meshes => meshes/wam}/wrist_yaw_link_fine.stl (100%) diff --git a/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml index 490c853..33fbb9d 100644 --- a/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml +++ b/alr_envs/mujoco/ball_in_a_cup/assets/biac_base.xml @@ -1,5 +1,5 @@ - +

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