-
Notifications
You must be signed in to change notification settings - Fork 191
/
record_sim_episodes.py
189 lines (162 loc) · 7.22 KB
/
record_sim_episodes.py
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
import time
import os
import numpy as np
import argparse
import matplotlib.pyplot as plt
import h5py
from constants import PUPPET_GRIPPER_POSITION_NORMALIZE_FN, SIM_TASK_CONFIGS
from ee_sim_env import make_ee_sim_env
from sim_env import make_sim_env, BOX_POSE
from scripted_policy import PickAndTransferPolicy, InsertionPolicy
import IPython
e = IPython.embed
def main(args):
"""
Generate demonstration data in simulation.
First rollout the policy (defined in ee space) in ee_sim_env. Obtain the joint trajectory.
Replace the gripper joint positions with the commanded joint position.
Replay this joint trajectory (as action sequence) in sim_env, and record all observations.
Save this episode of data, and continue to next episode of data collection.
"""
task_name = args['task_name']
dataset_dir = args['dataset_dir']
num_episodes = args['num_episodes']
onscreen_render = args['onscreen_render']
inject_noise = False
render_cam_name = 'angle'
if not os.path.isdir(dataset_dir):
os.makedirs(dataset_dir, exist_ok=True)
episode_len = SIM_TASK_CONFIGS[task_name]['episode_len']
camera_names = SIM_TASK_CONFIGS[task_name]['camera_names']
if task_name == 'sim_transfer_cube_scripted':
policy_cls = PickAndTransferPolicy
elif task_name == 'sim_insertion_scripted':
policy_cls = InsertionPolicy
else:
raise NotImplementedError
success = []
for episode_idx in range(num_episodes):
print(f'{episode_idx=}')
print('Rollout out EE space scripted policy')
# setup the environment
env = make_ee_sim_env(task_name)
ts = env.reset()
episode = [ts]
policy = policy_cls(inject_noise)
# setup plotting
if onscreen_render:
ax = plt.subplot()
plt_img = ax.imshow(ts.observation['images'][render_cam_name])
plt.ion()
for step in range(episode_len):
action = policy(ts)
ts = env.step(action)
episode.append(ts)
if onscreen_render:
plt_img.set_data(ts.observation['images'][render_cam_name])
plt.pause(0.002)
plt.close()
episode_return = np.sum([ts.reward for ts in episode[1:]])
episode_max_reward = np.max([ts.reward for ts in episode[1:]])
if episode_max_reward == env.task.max_reward:
print(f"{episode_idx=} Successful, {episode_return=}")
else:
print(f"{episode_idx=} Failed")
joint_traj = [ts.observation['qpos'] for ts in episode]
# replace gripper pose with gripper control
gripper_ctrl_traj = [ts.observation['gripper_ctrl'] for ts in episode]
for joint, ctrl in zip(joint_traj, gripper_ctrl_traj):
left_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[0])
right_ctrl = PUPPET_GRIPPER_POSITION_NORMALIZE_FN(ctrl[2])
joint[6] = left_ctrl
joint[6+7] = right_ctrl
subtask_info = episode[0].observation['env_state'].copy() # box pose at step 0
# clear unused variables
del env
del episode
del policy
# setup the environment
print('Replaying joint commands')
env = make_sim_env(task_name)
BOX_POSE[0] = subtask_info # make sure the sim_env has the same object configurations as ee_sim_env
ts = env.reset()
episode_replay = [ts]
# setup plotting
if onscreen_render:
ax = plt.subplot()
plt_img = ax.imshow(ts.observation['images'][render_cam_name])
plt.ion()
for t in range(len(joint_traj)): # note: this will increase episode length by 1
action = joint_traj[t]
ts = env.step(action)
episode_replay.append(ts)
if onscreen_render:
plt_img.set_data(ts.observation['images'][render_cam_name])
plt.pause(0.02)
episode_return = np.sum([ts.reward for ts in episode_replay[1:]])
episode_max_reward = np.max([ts.reward for ts in episode_replay[1:]])
if episode_max_reward == env.task.max_reward:
success.append(1)
print(f"{episode_idx=} Successful, {episode_return=}")
else:
success.append(0)
print(f"{episode_idx=} Failed")
plt.close()
"""
For each timestep:
observations
- images
- each_cam_name (480, 640, 3) 'uint8'
- qpos (14,) 'float64'
- qvel (14,) 'float64'
action (14,) 'float64'
"""
data_dict = {
'/observations/qpos': [],
'/observations/qvel': [],
'/action': [],
}
for cam_name in camera_names:
data_dict[f'/observations/images/{cam_name}'] = []
# because the replaying, there will be eps_len + 1 actions and eps_len + 2 timesteps
# truncate here to be consistent
joint_traj = joint_traj[:-1]
episode_replay = episode_replay[:-1]
# len(joint_traj) i.e. actions: max_timesteps
# len(episode_replay) i.e. time steps: max_timesteps + 1
max_timesteps = len(joint_traj)
while joint_traj:
action = joint_traj.pop(0)
ts = episode_replay.pop(0)
data_dict['/observations/qpos'].append(ts.observation['qpos'])
data_dict['/observations/qvel'].append(ts.observation['qvel'])
data_dict['/action'].append(action)
for cam_name in camera_names:
data_dict[f'/observations/images/{cam_name}'].append(ts.observation['images'][cam_name])
# HDF5
t0 = time.time()
dataset_path = os.path.join(dataset_dir, f'episode_{episode_idx}')
with h5py.File(dataset_path + '.hdf5', 'w', rdcc_nbytes=1024 ** 2 * 2) as root:
root.attrs['sim'] = True
obs = root.create_group('observations')
image = obs.create_group('images')
for cam_name in camera_names:
_ = image.create_dataset(cam_name, (max_timesteps, 480, 640, 3), dtype='uint8',
chunks=(1, 480, 640, 3), )
# compression='gzip',compression_opts=2,)
# compression=32001, compression_opts=(0, 0, 0, 0, 9, 1, 1), shuffle=False)
qpos = obs.create_dataset('qpos', (max_timesteps, 14))
qvel = obs.create_dataset('qvel', (max_timesteps, 14))
action = root.create_dataset('action', (max_timesteps, 14))
for name, array in data_dict.items():
root[name][...] = array
print(f'Saving: {time.time() - t0:.1f} secs\n')
print(f'Saved to {dataset_dir}')
print(f'Success: {np.sum(success)} / {len(success)}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--task_name', action='store', type=str, help='task_name', required=True)
parser.add_argument('--dataset_dir', action='store', type=str, help='dataset saving dir', required=True)
parser.add_argument('--num_episodes', action='store', type=int, help='num_episodes', required=False)
parser.add_argument('--onscreen_render', action='store_true')
main(vars(parser.parse_args()))