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record_sim_episodes.py
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record_sim_episodes.py
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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()))