Merge branch 'master' into reacher_env_cleanup
# Conflicts: # alr_envs/examples/examples_general.py
This commit is contained in:
Maximilian Huettenrauch
@@ -1,3 +0,0 @@
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from alr_envs.classic_control.hole_reacher.hole_reacher import HoleReacherEnv
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from alr_envs.classic_control.viapoint_reacher.viapoint_reacher import ViaPointReacher
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from alr_envs.classic_control.simple_reacher.simple_reacher import SimpleReacherEnv
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@@ -1,301 +0,0 @@
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from typing import Union
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import gym
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import matplotlib.pyplot as plt
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import numpy as np
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from gym.utils import seeding
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from matplotlib import patches
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from alr_envs.classic_control.utils import check_self_collision
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class HoleReacherEnv(gym.Env):
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def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
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hole_width: float = 1., random_start: bool = False, allow_self_collision: bool = False,
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allow_wall_collision: bool = False, collision_penalty: float = 1000):
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self.n_links = n_links
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self.link_lengths = np.ones((n_links, 1))
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self.random_start = random_start
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# provided initial parameters
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self.initial_x = hole_x # x-position of center of hole
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self.initial_width = hole_width # width of hole
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self.initial_depth = hole_depth # depth of hole
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# temp container for current env state
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self._tmp_x = None
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self._tmp_width = None
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self._tmp_depth = None
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self._goal = None # x-y coordinates for reaching the center at the bottom of the hole
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# collision
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self.allow_self_collision = allow_self_collision
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self.allow_wall_collision = allow_wall_collision
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self.collision_penalty = collision_penalty
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# state
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self._joints = None
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self._joint_angles = None
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self._angle_velocity = None
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self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
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self._start_vel = np.zeros(self.n_links)
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self._dt = 0.01
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action_bound = np.pi * np.ones((self.n_links,))
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state_bound = np.hstack([
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[np.pi] * self.n_links, # cos
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[np.pi] * self.n_links, # sin
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[np.inf] * self.n_links, # velocity
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[np.inf], # hole width
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# [np.inf], # hole depth
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[np.inf] * 2, # x-y coordinates of target distance
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[np.inf] # env steps, because reward start after n steps TODO: Maybe
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])
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self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
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self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
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# containers for plotting
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self.metadata = {'render.modes': ["human", "partial"]}
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self.fig = None
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self._steps = 0
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self.seed()
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@property
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def dt(self) -> Union[float, int]:
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return self._dt
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# @property
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# def start_pos(self):
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# return self._start_pos
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@property
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def current_pos(self):
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return self._joint_angles.copy()
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@property
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def current_vel(self):
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return self._angle_velocity.copy()
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def step(self, action: np.ndarray):
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"""
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A single step with an action in joint velocity space
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"""
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acc = (action - self._angle_velocity) / self.dt
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self._angle_velocity = action
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self._joint_angles = self._joint_angles + self.dt * self._angle_velocity # + 0.001 * np.random.randn(5)
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self._update_joints()
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reward, info = self._get_reward(acc)
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info.update({"is_collided": self._is_collided})
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self.end_effector_traj.append(np.copy(self.end_effector))
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self._steps += 1
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done = self._is_collided
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return self._get_obs().copy(), reward, done, info
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def reset(self):
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if self.random_start:
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# Maybe change more than first seed
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first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4)
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self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)])
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self._start_pos = self._joint_angles.copy()
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else:
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self._joint_angles = self._start_pos
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self._generate_hole()
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self._set_patches()
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self._angle_velocity = self._start_vel
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self._joints = np.zeros((self.n_links + 1, 2))
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self._update_joints()
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self._steps = 0
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self.end_effector_traj = []
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return self._get_obs().copy()
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def _generate_hole(self):
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self._tmp_x = self.np_random.uniform(1, 3.5, 1) if self.initial_x is None else np.copy(self.initial_x)
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self._tmp_width = self.np_random.uniform(0.15, 0.5, 1) if self.initial_width is None else np.copy(
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self.initial_width)
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# TODO we do not want this right now.
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self._tmp_depth = self.np_random.uniform(1, 1, 1) if self.initial_depth is None else np.copy(
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self.initial_depth)
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self._goal = np.hstack([self._tmp_x, -self._tmp_depth])
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def _update_joints(self):
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"""
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update _joints to get new end effector position. The other links are only required for rendering.
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Returns:
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"""
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line_points_in_taskspace = self._get_forward_kinematics(num_points_per_link=20)
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self._joints[1:, 0] = self._joints[0, 0] + line_points_in_taskspace[:, -1, 0]
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self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1]
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self_collision = False
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wall_collision = False
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if not self.allow_self_collision:
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self_collision = check_self_collision(line_points_in_taskspace)
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if np.any(np.abs(self._joint_angles) > np.pi) and not self.allow_self_collision:
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self_collision = True
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if not self.allow_wall_collision:
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wall_collision = self._check_wall_collision(line_points_in_taskspace)
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self._is_collided = self_collision or wall_collision
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def _get_reward(self, acc: np.ndarray):
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reward = 0
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# success = False
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if self._steps == 199 or self._is_collided:
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# return reward only in last time step
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# Episode also terminates when colliding, hence return reward
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dist = np.linalg.norm(self.end_effector - self._goal)
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# success = dist < 0.005 and not self._is_collided
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reward = - dist ** 2 - self.collision_penalty * self._is_collided
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reward -= 5e-8 * np.sum(acc ** 2)
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# info = {"is_success": success}
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return reward, {} # info
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def _get_obs(self):
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theta = self._joint_angles
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return np.hstack([
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np.cos(theta),
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np.sin(theta),
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self._angle_velocity,
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self._tmp_width,
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# self._tmp_hole_depth,
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self.end_effector - self._goal,
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self._steps
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])
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def _get_forward_kinematics(self, num_points_per_link=1):
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theta = self._joint_angles[:, None]
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intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1
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accumulated_theta = np.cumsum(theta, axis=0)
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end_effector = np.zeros(shape=(self.n_links, num_points_per_link, 2))
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x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points
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y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points
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end_effector[0, :, 0] = x[0, :]
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end_effector[0, :, 1] = y[0, :]
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for i in range(1, self.n_links):
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end_effector[i, :, 0] = x[i, :] + end_effector[i - 1, -1, 0]
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end_effector[i, :, 1] = y[i, :] + end_effector[i - 1, -1, 1]
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return np.squeeze(end_effector + self._joints[0, :])
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def _check_wall_collision(self, line_points):
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# all points that are before the hole in x
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r, c = np.where(line_points[:, :, 0] < (self._tmp_x - self._tmp_width / 2))
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# check if any of those points are below surface
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nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0)
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if nr_line_points_below_surface_before_hole > 0:
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return True
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# all points that are after the hole in x
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r, c = np.where(line_points[:, :, 0] > (self._tmp_x + self._tmp_width / 2))
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# check if any of those points are below surface
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nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0)
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if nr_line_points_below_surface_after_hole > 0:
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return True
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# all points that are above the hole
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r, c = np.where((line_points[:, :, 0] > (self._tmp_x - self._tmp_width / 2)) & (
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line_points[:, :, 0] < (self._tmp_x + self._tmp_width / 2)))
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# check if any of those points are below surface
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nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self._tmp_depth)
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if nr_line_points_below_surface_in_hole > 0:
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return True
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return False
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def render(self, mode='human'):
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if self.fig is None:
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# Create base figure once on the beginning. Afterwards only update
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plt.ion()
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self.fig = plt.figure()
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ax = self.fig.add_subplot(1, 1, 1)
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# limits
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lim = np.sum(self.link_lengths) + 0.5
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ax.set_xlim([-lim, lim])
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ax.set_ylim([-1.1, lim])
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self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
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self._set_patches()
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self.fig.show()
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self.fig.gca().set_title(
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f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
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if mode == "human":
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# arm
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self.line.set_data(self._joints[:, 0], self._joints[:, 1])
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self.fig.canvas.draw()
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self.fig.canvas.flush_events()
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elif mode == "partial":
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if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided:
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# Arm
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plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k',
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alpha=self._steps / 200)
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def _set_patches(self):
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if self.fig is not None:
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self.fig.gca().patches = []
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left_block = patches.Rectangle((-self.n_links, -self._tmp_depth),
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self.n_links + self._tmp_x - self._tmp_width / 2,
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self._tmp_depth,
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fill=True, edgecolor='k', facecolor='k')
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right_block = patches.Rectangle((self._tmp_x + self._tmp_width / 2, -self._tmp_depth),
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self.n_links - self._tmp_x + self._tmp_width / 2,
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self._tmp_depth,
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fill=True, edgecolor='k', facecolor='k')
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hole_floor = patches.Rectangle((self._tmp_x - self._tmp_width / 2, -self._tmp_depth),
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self._tmp_width,
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1 - self._tmp_depth,
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fill=True, edgecolor='k', facecolor='k')
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# Add the patch to the Axes
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self.fig.gca().add_patch(left_block)
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self.fig.gca().add_patch(right_block)
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self.fig.gca().add_patch(hole_floor)
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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@property
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def end_effector(self):
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return self._joints[self.n_links].T
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def close(self):
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super().close()
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if self.fig is not None:
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plt.close(self.fig)
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@@ -1,35 +0,0 @@
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from typing import Tuple, Union
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import numpy as np
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from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
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class HoleReacherMPWrapper(MPEnvWrapper):
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@property
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def active_obs(self):
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return np.hstack([
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[self.env.random_start] * self.env.n_links, # cos
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[self.env.random_start] * self.env.n_links, # sin
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[self.env.random_start] * self.env.n_links, # velocity
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[self.env.initial_width is None], # hole width
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# [self.env.hole_depth is None], # hole depth
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[True] * 2, # x-y coordinates of target distance
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[False] # env steps
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])
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@property
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def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
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return self.env.current_pos
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@property
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def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
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return self.env.current_vel
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@property
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def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
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raise ValueError("Goal position is not available and has to be learnt based on the environment.")
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@property
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def dt(self) -> Union[float, int]:
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return self.env.dt
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@@ -1,202 +0,0 @@
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from typing import Iterable, Union
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import gym
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import matplotlib.pyplot as plt
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import numpy as np
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from gym import spaces
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from gym.utils import seeding
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class SimpleReacherEnv(gym.Env):
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"""
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Simple Reaching Task without any physics simulation.
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Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions
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towards the end of the trajectory.
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"""
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def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True):
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super().__init__()
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self.link_lengths = np.ones(n_links)
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self.n_links = n_links
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self._dt = 0.1
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self.random_start = random_start
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# provided initial parameters
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self.inital_target = target
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# temp container for current env state
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self._goal = None
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self._joints = None
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self._joint_angles = None
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self._angle_velocity = None
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self._start_pos = np.zeros(self.n_links)
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self._start_vel = np.zeros(self.n_links)
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self.max_torque = 1
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self.steps_before_reward = 199
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action_bound = np.ones((self.n_links,)) * self.max_torque
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state_bound = np.hstack([
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[np.pi] * self.n_links, # cos
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[np.pi] * self.n_links, # sin
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[np.inf] * self.n_links, # velocity
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[np.inf] * 2, # x-y coordinates of target distance
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[np.inf] # env steps, because reward start after n steps TODO: Maybe
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])
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self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
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self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
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# containers for plotting
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self.metadata = {'render.modes': ["human"]}
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self.fig = None
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self._steps = 0
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self.seed()
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@property
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def dt(self) -> Union[float, int]:
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return self._dt
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# @property
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# def start_pos(self):
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# return self._start_pos
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@property
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def current_pos(self):
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return self._joint_angles
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@property
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def current_vel(self):
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return self._angle_velocity
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def step(self, action: np.ndarray):
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"""
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A single step with action in torque space
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"""
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# action = self._add_action_noise(action)
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ac = np.clip(action, -self.max_torque, self.max_torque)
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self._angle_velocity = self._angle_velocity + self.dt * ac
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self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
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self._update_joints()
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reward, info = self._get_reward(action)
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self._steps += 1
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done = False
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return self._get_obs().copy(), reward, done, info
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def reset(self):
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# TODO: maybe do initialisation more random?
|
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# Sample only orientation of first link, i.e. the arm is always straight.
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if self.random_start:
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self._joint_angles = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
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self._start_pos = self._joint_angles.copy()
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else:
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self._joint_angles = self._start_pos
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self._generate_goal()
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||||
self._angle_velocity = self._start_vel
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self._joints = np.zeros((self.n_links + 1, 2))
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self._update_joints()
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self._steps = 0
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||||
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||||
return self._get_obs().copy()
|
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def _update_joints(self):
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||||
"""
|
||||
update joints to get new end-effector position. The other links are only required for rendering.
|
||||
Returns:
|
||||
|
||||
"""
|
||||
angles = np.cumsum(self._joint_angles)
|
||||
x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)])
|
||||
self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0)
|
||||
|
||||
def _get_reward(self, action: np.ndarray):
|
||||
diff = self.end_effector - self._goal
|
||||
reward_dist = 0
|
||||
|
||||
if self._steps >= self.steps_before_reward:
|
||||
reward_dist -= np.linalg.norm(diff)
|
||||
# reward_dist = np.exp(-0.1 * diff ** 2).mean()
|
||||
# reward_dist = - (diff ** 2).mean()
|
||||
|
||||
reward_ctrl = (action ** 2).sum()
|
||||
reward = reward_dist - reward_ctrl
|
||||
return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
|
||||
|
||||
def _get_obs(self):
|
||||
theta = self._joint_angles
|
||||
return np.hstack([
|
||||
np.cos(theta),
|
||||
np.sin(theta),
|
||||
self._angle_velocity,
|
||||
self.end_effector - self._goal,
|
||||
self._steps
|
||||
])
|
||||
|
||||
def _generate_goal(self):
|
||||
|
||||
if self.inital_target is None:
|
||||
|
||||
total_length = np.sum(self.link_lengths)
|
||||
goal = np.array([total_length, total_length])
|
||||
while np.linalg.norm(goal) >= total_length:
|
||||
goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
|
||||
else:
|
||||
goal = np.copy(self.inital_target)
|
||||
|
||||
self._goal = goal
|
||||
|
||||
def render(self, mode='human'): # pragma: no cover
|
||||
if self.fig is None:
|
||||
# Create base figure once on the beginning. Afterwards only update
|
||||
plt.ion()
|
||||
self.fig = plt.figure()
|
||||
ax = self.fig.add_subplot(1, 1, 1)
|
||||
|
||||
# limits
|
||||
lim = np.sum(self.link_lengths) + 0.5
|
||||
ax.set_xlim([-lim, lim])
|
||||
ax.set_ylim([-lim, lim])
|
||||
|
||||
self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
|
||||
goal_pos = self._goal.T
|
||||
self.goal_point, = ax.plot(goal_pos[0], goal_pos[1], 'gx')
|
||||
self.goal_dist, = ax.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--')
|
||||
|
||||
self.fig.show()
|
||||
|
||||
self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
|
||||
|
||||
# goal
|
||||
goal_pos = self._goal.T
|
||||
if self._steps == 1:
|
||||
self.goal_point.set_data(goal_pos[0], goal_pos[1])
|
||||
|
||||
# arm
|
||||
self.line.set_data(self._joints[:, 0], self._joints[:, 1])
|
||||
|
||||
# distance between end effector and goal
|
||||
self.goal_dist.set_data([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]])
|
||||
|
||||
self.fig.canvas.draw()
|
||||
self.fig.canvas.flush_events()
|
||||
|
||||
def seed(self, seed=None):
|
||||
self.np_random, seed = seeding.np_random(seed)
|
||||
return [seed]
|
||||
|
||||
def close(self):
|
||||
del self.fig
|
||||
|
||||
@property
|
||||
def end_effector(self):
|
||||
return self._joints[self.n_links].T
|
||||
@@ -1,33 +0,0 @@
|
||||
from typing import Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
|
||||
|
||||
|
||||
class SimpleReacherMPWrapper(MPEnvWrapper):
|
||||
@property
|
||||
def active_obs(self):
|
||||
return np.hstack([
|
||||
[self.env.random_start] * self.env.n_links, # cos
|
||||
[self.env.random_start] * self.env.n_links, # sin
|
||||
[self.env.random_start] * self.env.n_links, # velocity
|
||||
[True] * 2, # x-y coordinates of target distance
|
||||
[False] # env steps
|
||||
])
|
||||
|
||||
@property
|
||||
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
return self.env.current_pos
|
||||
|
||||
@property
|
||||
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
return self.env.current_vel
|
||||
|
||||
@property
|
||||
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
|
||||
|
||||
@property
|
||||
def dt(self) -> Union[float, int]:
|
||||
return self.env.dt
|
||||
@@ -1,18 +0,0 @@
|
||||
def ccw(A, B, C):
|
||||
return (C[1] - A[1]) * (B[0] - A[0]) - (B[1] - A[1]) * (C[0] - A[0]) > 1e-12
|
||||
|
||||
|
||||
def intersect(A, B, C, D):
|
||||
"""
|
||||
Checks whether line segments AB and CD intersect
|
||||
"""
|
||||
return ccw(A, C, D) != ccw(B, C, D) and ccw(A, B, C) != ccw(A, B, D)
|
||||
|
||||
|
||||
def check_self_collision(line_points):
|
||||
"""Checks whether line segments intersect"""
|
||||
for i, line1 in enumerate(line_points):
|
||||
for line2 in line_points[i + 2:, :, :]:
|
||||
if intersect(line1[0], line1[-1], line2[0], line2[-1]):
|
||||
return True
|
||||
return False
|
||||
@@ -1,294 +0,0 @@
|
||||
from typing import Iterable, Union
|
||||
|
||||
import gym
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from gym.utils import seeding
|
||||
|
||||
from alr_envs.classic_control.utils import check_self_collision
|
||||
|
||||
|
||||
class ViaPointReacher(gym.Env):
|
||||
|
||||
def __init__(self, n_links, random_start: bool = False, via_target: Union[None, Iterable] = None,
|
||||
target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=1000):
|
||||
|
||||
self.n_links = n_links
|
||||
self.link_lengths = np.ones((n_links, 1))
|
||||
|
||||
self.random_start = random_start
|
||||
|
||||
# provided initial parameters
|
||||
self.intitial_target = target # provided target value
|
||||
self.initial_via_target = via_target # provided via point target value
|
||||
|
||||
# temp container for current env state
|
||||
self._via_point = np.ones(2)
|
||||
self._goal = np.array((n_links, 0))
|
||||
|
||||
# collision
|
||||
self.allow_self_collision = allow_self_collision
|
||||
self.collision_penalty = collision_penalty
|
||||
|
||||
# state
|
||||
self._joints = None
|
||||
self._joint_angles = None
|
||||
self._angle_velocity = None
|
||||
self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
|
||||
self._start_vel = np.zeros(self.n_links)
|
||||
self.weight_matrix_scale = 1
|
||||
|
||||
self._dt = 0.01
|
||||
|
||||
action_bound = np.pi * np.ones((self.n_links,))
|
||||
state_bound = np.hstack([
|
||||
[np.pi] * self.n_links, # cos
|
||||
[np.pi] * self.n_links, # sin
|
||||
[np.inf] * self.n_links, # velocity
|
||||
[np.inf] * 2, # x-y coordinates of via point distance
|
||||
[np.inf] * 2, # x-y coordinates of target distance
|
||||
[np.inf] # env steps, because reward start after n steps
|
||||
])
|
||||
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)
|
||||
|
||||
# containers for plotting
|
||||
self.metadata = {'render.modes': ["human", "partial"]}
|
||||
self.fig = None
|
||||
|
||||
self._steps = 0
|
||||
self.seed()
|
||||
|
||||
@property
|
||||
def dt(self):
|
||||
return self._dt
|
||||
|
||||
# @property
|
||||
# def start_pos(self):
|
||||
# return self._start_pos
|
||||
|
||||
@property
|
||||
def current_pos(self):
|
||||
return self._joint_angles.copy()
|
||||
|
||||
@property
|
||||
def current_vel(self):
|
||||
return self._angle_velocity.copy()
|
||||
|
||||
def step(self, action: np.ndarray):
|
||||
"""
|
||||
a single step with an action in joint velocity space
|
||||
"""
|
||||
vel = action
|
||||
self._angle_velocity = vel
|
||||
self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
|
||||
self._update_joints()
|
||||
|
||||
acc = (vel - self._angle_velocity) / self.dt
|
||||
reward, info = self._get_reward(acc)
|
||||
|
||||
info.update({"is_collided": self._is_collided})
|
||||
|
||||
self._steps += 1
|
||||
done = self._is_collided
|
||||
|
||||
return self._get_obs().copy(), reward, done, info
|
||||
|
||||
def reset(self):
|
||||
|
||||
if self.random_start:
|
||||
# Maybe change more than dirst seed
|
||||
first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4)
|
||||
self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)])
|
||||
self._start_pos = self._joint_angles.copy()
|
||||
else:
|
||||
self._joint_angles = self._start_pos
|
||||
|
||||
self._generate_goal()
|
||||
|
||||
self._angle_velocity = self._start_vel
|
||||
self._joints = np.zeros((self.n_links + 1, 2))
|
||||
self._update_joints()
|
||||
self._steps = 0
|
||||
|
||||
return self._get_obs().copy()
|
||||
|
||||
def _generate_goal(self):
|
||||
# TODO: Maybe improve this later, this can yield quite a lot of invalid settings
|
||||
|
||||
total_length = np.sum(self.link_lengths)
|
||||
|
||||
# rejection sampled point in inner circle with 0.5*Radius
|
||||
if self.initial_via_target is None:
|
||||
via_target = np.array([total_length, total_length])
|
||||
while np.linalg.norm(via_target) >= 0.5 * total_length:
|
||||
via_target = self.np_random.uniform(low=-0.5 * total_length, high=0.5 * total_length, size=2)
|
||||
else:
|
||||
via_target = np.copy(self.initial_via_target)
|
||||
|
||||
# rejection sampled point in outer circle
|
||||
if self.intitial_target is None:
|
||||
goal = np.array([total_length, total_length])
|
||||
while np.linalg.norm(goal) >= total_length or np.linalg.norm(goal) <= 0.5 * total_length:
|
||||
goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
|
||||
else:
|
||||
goal = np.copy(self.intitial_target)
|
||||
|
||||
self._via_point = via_target
|
||||
self._goal = goal
|
||||
|
||||
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
|
||||
|
||||
if not self.allow_self_collision:
|
||||
self_collision = check_self_collision(line_points_in_taskspace)
|
||||
if np.any(np.abs(self._joint_angles) > np.pi):
|
||||
self_collision = True
|
||||
|
||||
self._is_collided = self_collision
|
||||
|
||||
def _get_reward(self, acc):
|
||||
success = False
|
||||
reward = -np.inf
|
||||
if not self._is_collided:
|
||||
dist = np.inf
|
||||
# return intermediate reward for via point
|
||||
if self._steps == 100:
|
||||
dist = np.linalg.norm(self.end_effector - self._via_point)
|
||||
# return reward in last time step for goal
|
||||
elif self._steps == 199:
|
||||
dist = np.linalg.norm(self.end_effector - self._goal)
|
||||
|
||||
success = dist < 0.005
|
||||
else:
|
||||
# Episode terminates when colliding, hence return reward
|
||||
dist = np.linalg.norm(self.end_effector - self._goal)
|
||||
reward = -self.collision_penalty
|
||||
|
||||
reward -= dist ** 2
|
||||
reward -= 5e-8 * np.sum(acc ** 2)
|
||||
info = {"is_success": success}
|
||||
|
||||
return reward, info
|
||||
|
||||
def _get_obs(self):
|
||||
theta = self._joint_angles
|
||||
return np.hstack([
|
||||
np.cos(theta),
|
||||
np.sin(theta),
|
||||
self._angle_velocity,
|
||||
self.end_effector - self._via_point,
|
||||
self.end_effector - self._goal,
|
||||
self._steps
|
||||
])
|
||||
|
||||
def get_forward_kinematics(self, num_points_per_link=1):
|
||||
theta = self._joint_angles[:, None]
|
||||
|
||||
intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1
|
||||
|
||||
accumulated_theta = np.cumsum(theta, axis=0)
|
||||
|
||||
endeffector = np.zeros(shape=(self.n_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.n_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 render(self, mode='human'):
|
||||
goal_pos = self._goal.T
|
||||
via_pos = self._via_point.T
|
||||
|
||||
if self.fig is None:
|
||||
# Create base figure once on the beginning. Afterwards only update
|
||||
plt.ion()
|
||||
self.fig = plt.figure()
|
||||
ax = self.fig.add_subplot(1, 1, 1)
|
||||
|
||||
# limits
|
||||
lim = np.sum(self.link_lengths) + 0.5
|
||||
ax.set_xlim([-lim, lim])
|
||||
ax.set_ylim([-lim, lim])
|
||||
|
||||
self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
|
||||
self.goal_point_plot, = ax.plot(goal_pos[0], goal_pos[1], 'go')
|
||||
self.via_point_plot, = ax.plot(via_pos[0], via_pos[1], 'gx')
|
||||
|
||||
self.fig.show()
|
||||
|
||||
self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
|
||||
|
||||
if mode == "human":
|
||||
# goal
|
||||
if self._steps == 1:
|
||||
self.goal_point_plot.set_data(goal_pos[0], goal_pos[1])
|
||||
self.via_point_plot.set_data(via_pos[0], goal_pos[1])
|
||||
|
||||
# arm
|
||||
self.line.set_data(self._joints[:, 0], self._joints[:, 1])
|
||||
|
||||
self.fig.canvas.draw()
|
||||
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.n_links, self.n_links), plt.ylim(-1, self.n_links)
|
||||
# Arm
|
||||
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
|
||||
|
||||
plt.pause(0.01)
|
||||
|
||||
def seed(self, seed=None):
|
||||
self.np_random, seed = seeding.np_random(seed)
|
||||
return [seed]
|
||||
|
||||
@property
|
||||
def end_effector(self):
|
||||
return self._joints[self.n_links].T
|
||||
|
||||
def close(self):
|
||||
if self.fig is not None:
|
||||
plt.close(self.fig)
|
||||
@@ -1,34 +0,0 @@
|
||||
from typing import Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from mp_env_api.interface_wrappers.mp_env_wrapper import MPEnvWrapper
|
||||
|
||||
|
||||
class ViaPointReacherMPWrapper(MPEnvWrapper):
|
||||
@property
|
||||
def active_obs(self):
|
||||
return np.hstack([
|
||||
[self.env.random_start] * self.env.n_links, # cos
|
||||
[self.env.random_start] * self.env.n_links, # sin
|
||||
[self.env.random_start] * self.env.n_links, # velocity
|
||||
[self.env.initial_via_target is None] * 2, # x-y coordinates of via point distance
|
||||
[True] * 2, # x-y coordinates of target distance
|
||||
[False] # env steps
|
||||
])
|
||||
|
||||
@property
|
||||
def current_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
return self.env.current_pos
|
||||
|
||||
@property
|
||||
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
return self.env.current_vel
|
||||
|
||||
@property
|
||||
def goal_pos(self) -> Union[float, int, np.ndarray, Tuple]:
|
||||
raise ValueError("Goal position is not available and has to be learnt based on the environment.")
|
||||
|
||||
@property
|
||||
def dt(self) -> Union[float, int]:
|
||||
return self.env.dt
|
||||
Reference in New Issue
Block a user