1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
|
"""
Classic cart-pole system implemented by Rich Sutton et al.
Copied from https://webdocs.cs.ualberta.ca/~sutton/book/code/pole.c
"""
import os, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
os.sys.path.insert(0, parentdir)
import logging
import math
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
import time
import subprocess
import pybullet as p2
import pybullet_data
from pybullet_utils import bullet_client as bc
from pkg_resources import parse_version
from pybullet_envs.deep_mimic.env.pybullet_deep_mimic_env import PyBulletDeepMimicEnv, InitializationStrategy
from pybullet_utils.arg_parser import ArgParser
from pybullet_utils.logger import Logger
from typing import Optional
logger = logging.getLogger(__name__)
class HumanoidDeepBulletEnv(gym.Env):
"""Base Gym environment for DeepMimic."""
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
def __init__(self, renders=False, arg_file='', test_mode=False,
time_step=1./240,
rescale_actions=True,
rescale_observations=True):
"""
Args:
test_mode (bool): in test mode, the `reset()` method will always set the mocap clip time
to 0.
time_step (float): physics time step.
"""
self._arg_parser = ArgParser()
Logger.print2("===========================================================")
succ = False
if (arg_file != ''):
path = pybullet_data.getDataPath() + "/args/" + arg_file
succ = self._arg_parser.load_file(path)
Logger.print2(arg_file)
assert succ, Logger.print2('Failed to load args from: ' + arg_file)
self._p: Optional[BulletClient] = None
self._time_step = time_step
self._internal_env: Optional[PyBulletDeepMimicEnv] = None
self._renders = renders
self._discrete_actions = False
self._arg_file = arg_file
self._render_height = 400
self._render_width = 640
self._rescale_actions = rescale_actions
self._rescale_observations = rescale_observations
self.agent_id = -1
self._numSteps = None
self.test_mode = test_mode
if self.test_mode:
print("Environment running in TEST mode")
self.reset()
# Query the policy at 30Hz
self.policy_query_30 = True
if self.policy_query_30:
self._policy_step = 1./30
else:
self._policy_step = 1./240
self._num_env_steps = int(self._policy_step / self._time_step)
self.theta_threshold_radians = 12 * 2 * math.pi / 360
self.x_threshold = 0.4 #2.4
high = np.array([
self.x_threshold * 2,
np.finfo(np.float32).max, self.theta_threshold_radians * 2,
np.finfo(np.float32).max
])
ctrl_size = 43 #numDof
root_size = 7 # root
action_dim = ctrl_size - root_size
action_bound_min = np.array([
-4.79999999999, -1.00000000000, -1.00000000000, -1.00000000000, -4.00000000000,
-1.00000000000, -1.00000000000, -1.00000000000, -7.77999999999, -1.00000000000,
-1.000000000, -1.000000000, -7.850000000, -6.280000000, -1.000000000, -1.000000000,
-1.000000000, -12.56000000, -1.000000000, -1.000000000, -1.000000000, -4.710000000,
-7.779999999, -1.000000000, -1.000000000, -1.000000000, -7.850000000, -6.280000000,
-1.000000000, -1.000000000, -1.000000000, -8.460000000, -1.000000000, -1.000000000,
-1.000000000, -4.710000000
])
#print("len(action_bound_min)=",len(action_bound_min))
action_bound_max = np.array([
4.799999999, 1.000000000, 1.000000000, 1.000000000, 4.000000000, 1.000000000, 1.000000000,
1.000000000, 8.779999999, 1.000000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000,
1.0000000, 1.0000000, 1.0000000, 12.560000, 1.0000000, 1.0000000, 1.0000000, 7.8500000,
8.7799999, 1.0000000, 1.0000000, 1.0000000, 4.7100000, 6.2800000, 1.0000000, 1.0000000,
1.0000000, 10.100000, 1.0000000, 1.0000000, 1.0000000, 7.8500000
])
#print("len(action_bound_max)=",len(action_bound_max))
self.action_space = spaces.Box(action_bound_min, action_bound_max)
observation_min = np.array([0.0]+[-100.0]+[-4.0]*105+[-500.0]*90)
observation_max = np.array([1.0]+[100.0]+[4.0]*105+[500.0]*90)
state_size = 197
self.observation_space = spaces.Box(observation_min, observation_min, dtype=np.float32)
self.seed()
self.viewer = None
self._configure()
def _configure(self, display=None):
self.display = display
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def step(self, action):
agent_id = self.agent_id
if self._rescale_actions:
# Rescale the action
mean = -self._action_offset
std = 1./self._action_scale
action = action * std + mean
# Record reward
reward = self._internal_env.calc_reward(agent_id)
# Apply control action
self._internal_env.set_action(agent_id, action)
start_time = self._internal_env.t
# step sim
for i in range(self._num_env_steps):
self._internal_env.update(self._time_step)
elapsed_time = self._internal_env.t - start_time
self._numSteps += 1
# Record state
self.state = self._internal_env.record_state(agent_id)
if self._rescale_observations:
state = np.array(self.state)
mean = -self._state_offset
std = 1./self._state_scale
state = (state - mean) / (std + 1e-8)
# Record done
done = self._internal_env.is_episode_end()
info = {}
return state, reward, done, info
def reset(self):
# use the initialization strategy
if self._internal_env is None:
if self.test_mode:
init_strat = InitializationStrategy.START
else:
init_strat = InitializationStrategy.RANDOM
self._internal_env = PyBulletDeepMimicEnv(self._arg_parser, self._renders,
time_step=self._time_step,
init_strategy=init_strat)
self._internal_env.reset()
self._p = self._internal_env._pybullet_client
agent_id = self.agent_id # unused here
self._state_offset = self._internal_env.build_state_offset(self.agent_id)
self._state_scale = self._internal_env.build_state_scale(self.agent_id)
self._action_offset = self._internal_env.build_action_offset(self.agent_id)
self._action_scale = self._internal_env.build_action_scale(self.agent_id)
self._numSteps = 0
# Record state
self.state = self._internal_env.record_state(agent_id)
# return state as ndarray
state = np.array(self.state)
if self._rescale_observations:
mean = -self._state_offset
std = 1./self._state_scale
state = (state - mean) / (std + 1e-8)
return state
def render(self, mode='human', close=False):
if mode == "human":
self._renders = True
if mode != "rgb_array":
return np.array([])
human = self._internal_env._humanoid
base_pos, orn = self._p.getBasePositionAndOrientation(human._sim_model)
base_pos = np.asarray(base_pos)
# track the position
base_pos[1] += 0.3
rpy = self._p.getEulerFromQuaternion(orn) # rpy, in radians
rpy = 180 / np.pi * np.asarray(rpy) # convert rpy in degrees
self._cam_dist = 3
self._cam_pitch = 0.3
self._cam_yaw = 0
if (not self._p == None):
view_matrix = self._p.computeViewMatrixFromYawPitchRoll(
cameraTargetPosition=base_pos,
distance=self._cam_dist,
yaw=self._cam_yaw,
pitch=self._cam_pitch,
roll=0,
upAxisIndex=1)
proj_matrix = self._p.computeProjectionMatrixFOV(fov=60,
aspect=float(self._render_width) / self._render_height,
nearVal=0.1,
farVal=100.0)
(_, _, px, _, _) = self._p.getCameraImage(
width=self._render_width,
height=self._render_height,
renderer=self._p.ER_BULLET_HARDWARE_OPENGL,
viewMatrix=view_matrix,
projectionMatrix=proj_matrix)
# self._p.resetDebugVisualizerCamera(
# cameraDistance=2 * self._cam_dist,
# cameraYaw=self._cam_yaw,
# cameraPitch=self._cam_pitch,
# cameraTargetPosition=base_pos
# )
else:
px = np.array([[[255,255,255,255]]*self._render_width]*self._render_height, dtype=np.uint8)
rgb_array = np.array(px, dtype=np.uint8)
rgb_array = np.reshape(np.array(px), (self._render_height, self._render_width, -1))
rgb_array = rgb_array[:, :, :3]
return rgb_array
def configure(self, args):
pass
def close(self):
pass
class HumanoidDeepMimicBackflipBulletEnv(HumanoidDeepBulletEnv):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
def __init__(self, renders=False):
# start the bullet physics server
HumanoidDeepBulletEnv.__init__(self, renders, arg_file="run_humanoid3d_backflip_args.txt")
class HumanoidDeepMimicWalkBulletEnv(HumanoidDeepBulletEnv):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
def __init__(self, renders=False):
# start the bullet physics server
HumanoidDeepBulletEnv.__init__(self, renders, arg_file="run_humanoid3d_walk_args.txt")
class CartPoleContinuousBulletEnv5(HumanoidDeepBulletEnv):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50}
def __init__(self, renders=False):
# start the bullet physics server
HumanoidDeepBulletEnv.__init__(self, renders, arg_file="")
|