193 lines
7.3 KiB
Python
193 lines
7.3 KiB
Python
from typing import Iterable, Union, Tuple, Optional, Any, Dict
|
|
|
|
import gymnasium as gym
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from gymnasium import spaces
|
|
from gymnasium.core import ObsType
|
|
|
|
from fancy_gym.envs.classic_control.base_reacher.base_reacher_direct import BaseReacherDirectEnv
|
|
from . import MPWrapper
|
|
|
|
|
|
class ViaPointReacherEnv(BaseReacherDirectEnv):
|
|
|
|
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, **kwargs):
|
|
|
|
super().__init__(n_links, random_start, allow_self_collision, **kwargs)
|
|
|
|
# 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.collision_penalty = collision_penalty
|
|
|
|
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.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
|
|
|
|
# @property
|
|
# def start_pos(self):
|
|
# return self._start_pos
|
|
|
|
def reset(self, *, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None) \
|
|
-> Tuple[ObsType, Dict[str, Any]]:
|
|
# Reset twice to ensure we return obs after generating goal and generating goal after executing seeded reset.
|
|
# (Env will not behave deterministic otherwise)
|
|
# Yes, there is probably a more elegant solution to this problem...
|
|
self._generate_goal()
|
|
super().reset(seed=seed, options=options)
|
|
self._generate_goal()
|
|
return super().reset(seed=seed, options=options)
|
|
|
|
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 _get_reward(self, acc):
|
|
success = False
|
|
reward = -np.inf
|
|
|
|
if not self.allow_self_collision:
|
|
self._is_collided = self._check_self_collision()
|
|
|
|
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,
|
|
"is_collided": self._is_collided,
|
|
"end_effector": np.copy(self.end_effector)}
|
|
|
|
return reward, info
|
|
|
|
def _terminate(self, info):
|
|
return info["is_collided"]
|
|
|
|
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
|
|
]).astype(np.float32)
|
|
|
|
def _check_collisions(self) -> bool:
|
|
return self._check_self_collision()
|
|
|
|
def render(self):
|
|
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 self.render_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 self.render_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 self.render_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)
|