231 lines
7.4 KiB
Python
231 lines
7.4 KiB
Python
import gym
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import matplotlib.pyplot as plt
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import numpy as np
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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, allow_self_collision=False, collision_penalty=1000):
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self.num_links = n_links
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self.link_lengths = np.ones((n_links, 1))
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# task
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self.via_point = np.ones(2)
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self.goal_point = 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.num_links - 1)])
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self.start_vel = np.zeros(self.num_links)
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self.weight_matrix_scale = 1
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self._steps = 0
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self.dt = 0.01
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# self.time_limit = 2
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action_bound = np.pi * np.ones((self.num_links,))
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state_bound = np.hstack([
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[np.pi] * self.num_links, # cos
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[np.pi] * self.num_links, # sin
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[np.inf] * self.num_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 = 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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self.fig = None
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@property
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def end_effector(self):
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return self._joints[self.num_links].T
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def configure(self, context):
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pass
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def reset(self):
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self._joint_angles = self.start_pos
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self._angle_velocity = self.start_vel
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self._joints = np.zeros((self.num_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 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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acc = (vel - self._angle_velocity) / self.dt
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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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dist_reward = 0
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if not self._is_collided:
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if self._steps == 100:
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dist_reward = np.linalg.norm(self.end_effector - self.via_point)
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elif self._steps == 199:
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dist_reward = np.linalg.norm(self.end_effector - self.goal_point)
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# TODO: Do we need that?
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reward = - dist_reward ** 2
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reward -= 5e-8 * np.sum(acc**2)
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if self._is_collided:
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reward -= self.collision_penalty
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info = {"is_collided": self._is_collided}
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self._steps += 1
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# done = self._steps * self.dt > self.time_limit or self._is_collided
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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 _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_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_point,
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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.num_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.num_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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if self.fig is None:
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self.fig = plt.figure()
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# plt.ion()
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# plt.pause(0.01)
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else:
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plt.figure(self.fig.number)
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if mode == "human":
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plt.cla()
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plt.title(f"Iteration: {self._steps}")
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# Arm
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plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
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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([-lim, lim])
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# plt.draw()
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plt.pause(1e-4) # pushes window to foreground, which is annoying.
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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.num_links, self.num_links), plt.ylim(-1, self.num_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 close(self):
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if self.fig is not None:
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plt.close(self.fig)
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if __name__ == '__main__':
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nl = 5
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render_mode = "human" # "human" or "partial" or "final"
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env = ViaPointReacher(n_links=nl, allow_self_collision=False)
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env.reset()
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env.render(mode=render_mode)
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for i in range(300):
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# objective.load_result("/tmp/cma")
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# test with random actions
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ac = env.action_space.sample()
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# ac[0] += np.pi/2
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obs, rew, d, info = env.step(ac)
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env.render(mode=render_mode)
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print(rew)
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if d:
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break
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env.close()
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