wangzixiang
/
lerobot
mirror of https://github.com/huggingface/lerobot.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Fixed bug in the action scale of the intervention actions and offline dataset actions. (scale by inverse delta) 

Co-authored-by: Adil Zouitine <adizouitinegm@gmail.com>
This commit is contained in:
Michel Aractingi 2025-02-14 15:17:16 +01:00
parent 36711d766a
commit 7ae368e983
5 changed files with 42 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
lerobot/configs/policy/sac_real.yaml
View File

@ -8,8 +8,8 @@
# env.gym.obs_type=environment_state_agent_pos \
seed: 1
dataset_repo_id: aractingi/push_cube_square_offline_demo_cropped_resized
#aractingi/push_cube_square_light_offline_demo_cropped_resized
dataset_repo_id: aractingi/push_cube_overfit_cropped_resized
#aractingi/push_cube_square_offline_demo_cropped_resized
training:
# Offline training dataloader

4
lerobot/scripts/server/buffer.py
View File

@ -209,6 +209,7 @@ class ReplayBuffer:
state_keys: Optional[Sequence[str]] = None,
capacity: Optional[int] = None,
action_mask: Optional[Sequence[int]] = None,
action_delta: Optional[float] = None,
) -> "ReplayBuffer":
"""
Convert a LeRobotDataset into a ReplayBuffer.
@ -249,6 +250,9 @@ class ReplayBuffer:
else:
data["action"] = data["action"][:, action_mask]
if action_delta is not None:
data["action"] = data["action"] / action_delta
replay_buffer.add(
state=data["state"],
action=data["action"],

8
lerobot/scripts/server/crop_dataset_roi.py
View File

@ -260,12 +260,8 @@ if __name__ == "__main__":
rois = json.load(f)
# rois = {
# "observation.images.side": (92, 123, 379, 349),
# "observation.images.front": (109, 37, 361, 557),
# }
# rois = {
# "observation.images.front": [109, 37, 361, 557],
# "observation.images.side": [94, 161, 372, 315],
# "observation.images.front": [102, 43, 358, 523],
# "observation.images.side": [92, 123, 379, 349],
# }
# Print the selected rectangular ROIs

41
lerobot/scripts/server/gym_manipulator.py
View File

@ -213,18 +213,18 @@ class HILSerlRobotEnv(gym.Env):
# When applying the delta action space, convert teleop absolute values to relative differences.
if self.use_delta_action_space:
teleop_action = teleop_action - self.current_joint_positions
if torch.any(teleop_action < -self.delta_relative_bounds_size * self.delta) and torch.any(
teleop_action > self.delta_relative_bounds_size
teleop_action = (teleop_action - self.current_joint_positions) / self.delta
if torch.any(teleop_action < -self.relative_bounds_size) and torch.any(
teleop_action > self.relative_bounds_size
):
logging.debug(
f"Relative teleop delta exceeded bounds {self.delta_relative_bounds_size}, teleop_action {teleop_action}\n"
f"lower bounds condition {teleop_action < -self.delta_relative_bounds_size}\n"
f"upper bounds condition {teleop_action > self.delta_relative_bounds_size}"
f"Relative teleop delta exceeded bounds {self.relative_bounds_size}, teleop_action {teleop_action}\n"
f"lower bounds condition {teleop_action < -self.relative_bounds_size}\n"
f"upper bounds condition {teleop_action > self.relative_bounds_size}"
)
teleop_action = torch.clamp(
teleop_action, -self.delta_relative_bounds_size, self.delta_relative_bounds_size
teleop_action, -self.relative_bounds_size, self.relative_bounds_size
)
# NOTE: To mimic the shape of a neural network output, we add a batch dimension to the teleop action.
if teleop_action.dim() == 1:
@ -312,7 +312,7 @@ class RewardWrapper(gym.Wrapper):
start_time = time.perf_counter()
with torch.inference_mode():
reward = (
self.reward_classifier.predict_reward(images, threshold=0.6)
self.reward_classifier.predict_reward(images, threshold=0.8)
if self.reward_classifier is not None
else 0.0
)
@ -726,6 +726,24 @@ def get_classifier(pretrained_path, config_path, device="mps"):
return model
def replay_episode(env, repo_id, root=None, episode=0):
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
local_files_only = root is not None
dataset = LeRobotDataset(repo_id, root=root, episodes=[episode], local_files_only=local_files_only)
actions = dataset.hf_dataset.select_columns("action")
for idx in range(dataset.num_frames):
start_episode_t = time.perf_counter()
action = actions[idx]["action"][:4]
print(action)
env.step((action / env.unwrapped.delta, False))
dt_s = time.perf_counter() - start_episode_t
busy_wait(1 / 10 - dt_s)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--fps", type=int, default=30, help="control frequency")
@ -776,6 +794,9 @@ if __name__ == "__main__":
parser.add_argument("--env-overrides", type=str, default=None, help="Overrides for the env yaml file")
parser.add_argument("--control-time-s", type=float, default=20, help="Maximum episode length in seconds")
parser.add_argument("--reset-follower-pos", type=int, default=1, help="Reset follower between episodes")
parser.add_argument("--replay-repo-id", type=str, default=None, help="Repo ID of the episode to replay")
parser.add_argument("--replay-root", type=str, default=None, help="Root of the dataset to replay")
parser.add_argument("--replay-episode", type=int, default=0, help="Episode to replay")
args = parser.parse_args()
robot_cfg = init_hydra_config(args.robot_path, args.robot_overrides)
@ -795,6 +816,10 @@ if __name__ == "__main__":
env.reset()
if args.replay_repo_id is not None:
replay_episode(env, args.replay_repo_id, root=args.replay_root, episode=args.replay_episode)
exit()
# Retrieve the robot's action space for joint commands.
action_space_robot = env.action_space.spaces[0]

1
lerobot/scripts/server/learner_server.py
View File

@ -600,6 +600,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
device=device,
state_keys=cfg.policy.input_shapes.keys(),
action_mask=active_action_dims,
action_delta=cfg.env.wrapper.delta_action,
)
batch_size: int = batch_size // 2 # We will sample from both replay buffer