matched file structure of classic control with other tasks
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ottofabian
@@ -0,0 +1,282 @@
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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.utils import seeding
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from alr_envs.classic_control.utils import check_self_collision
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class ViaPointReacher(gym.Env):
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def __init__(self, n_links, random_start: bool = True, via_target: Union[None, Iterable] = None,
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target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=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.target = target # provided target value
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self.via_target = via_target # provided via point target value
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# temp container for current env state
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self._via_point = np.ones(2)
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self._goal = np.array((n_links, 0))
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# collision
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self.allow_self_collision = allow_self_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.weight_matrix_scale = 1
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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] * 2, # x-y coordinates of via point distance
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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
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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):
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return self._dt
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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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vel = action
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self._angle_velocity = vel
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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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acc = (vel - self._angle_velocity) / self.dt
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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._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 dirst 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_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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return self._get_obs().copy()
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def _generate_goal(self):
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# TODO: Maybe improve this later, this can yield quite a lot of invalid settings
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total_length = np.sum(self.link_lengths)
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# rejection sampled point in inner circle with 0.5*Radius
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if self.via_target is None:
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via_target = np.array([total_length, total_length])
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while np.linalg.norm(via_target) >= 0.5 * total_length:
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via_target = self.np_random.uniform(low=-0.5 * total_length, high=0.5 * total_length, size=2)
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else:
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via_target = np.copy(self.via_target)
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# rejection sampled point in outer circle
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if self.target is None:
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goal = np.array([total_length, total_length])
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while np.linalg.norm(goal) >= total_length or np.linalg.norm(goal) <= 0.5 * total_length:
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goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
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else:
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goal = np.copy(self.target)
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self.via_target = via_target
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self._goal = goal
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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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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):
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self_collision = True
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self._is_collided = self_collision
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def _get_reward(self, acc):
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success = False
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reward = -np.inf
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if not self._is_collided:
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dist = np.inf
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# return intermediate reward for via point
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if self._steps == 100:
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dist = np.linalg.norm(self.end_effector - self._via_point)
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# return reward in last time step for goal
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elif self._steps == 199:
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dist = np.linalg.norm(self.end_effector - self._goal)
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success = dist < 0.005
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else:
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# Episode terminates when colliding, hence return reward
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dist = np.linalg.norm(self.end_effector - self._goal)
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reward = -self.collision_penalty
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reward -= dist ** 2
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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.end_effector - self._via_point,
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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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endeffector = 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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endeffector[0, :, 0] = x[0, :]
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endeffector[0, :, 1] = y[0, :]
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for i in range(1, self.n_links):
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endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0]
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endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1]
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return np.squeeze(endeffector + self._joints[0, :])
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def render(self, mode='human'):
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goal_pos = self._goal.T
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via_pos = self._via_point.T
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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([-lim, lim])
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self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
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self.goal_point_plot, = ax.plot(goal_pos[0], goal_pos[1], 'go')
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self.via_point_plot, = ax.plot(via_pos[0], via_pos[1], 'gx')
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self.fig.show()
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self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}")
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if mode == "human":
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# goal
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if self._steps == 1:
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self.goal_point_plot.set_data(goal_pos[0], goal_pos[1])
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self.via_point_plot.set_data(via_pos[0], goal_pos[1])
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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 == 1:
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# fig, ax = plt.subplots()
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# Add the patch to the Axes
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[plt.gca().add_patch(rect) for rect in self.patches]
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# plt.pause(0.01)
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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', alpha=self._steps / 200)
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# ax.plot(line_points_in_taskspace[:, 0, 0],
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# line_points_in_taskspace[:, 0, 1],
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# line_points_in_taskspace[:, -1, 0],
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# line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200)
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lim = np.sum(self.link_lengths) + 0.5
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plt.xlim([-lim, lim])
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plt.ylim([-1.1, lim])
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plt.pause(0.01)
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elif mode == "final":
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if self._steps == 199 or self._is_collided:
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# fig, ax = plt.subplots()
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# Add the patch to the Axes
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[plt.gca().add_patch(rect) for rect in self.patches]
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plt.xlim(-self.n_links, self.n_links), plt.ylim(-1, self.n_links)
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# Arm
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plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
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plt.pause(0.01)
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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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if self.fig is not None:
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plt.close(self.fig)
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@@ -0,0 +1,29 @@
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from typing import Union
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import numpy as np
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from mp_env_api.envs.mp_env_wrapper import MPEnvWrapper
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class ViaPointReacherMPWrapper(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.via_target is None] * 2, # x-y coordinates of via point distance
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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 start_pos(self) -> Union[float, int, np.ndarray]:
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return self._start_pos
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@property
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def goal_pos(self) -> Union[float, int, np.ndarray]:
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raise ValueError("Goal position is not available and has to be learnt based on the environment.")
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def dt(self) -> Union[float, int]:
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return self.env.dt
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