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Merge branch 'main' into user/michel-aractingi/2025_06_30_dataset_v3

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Signed-off-by: Michel Aractingi <michel.aractingi@huggingface.co>
This commit is contained in:
Michel Aractingi
2025-07-02 18:22:59 +02:00
committed by GitHub
parent 69b1f7b118 63ddfefa08
commit 830a3b9f27
17 changed files with 372 additions and 599 deletions
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9
src/lerobot/scripts/find_joint_limits.py
View File

@@ -50,6 +50,7 @@ from lerobot.teleoperators import ( # noqa: F401
make_teleoperator_from_config,
so100_leader,
)
from lerobot.utils.robot_utils import busy_wait
@dataclass
@@ -76,12 +77,12 @@ def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig):
# Note to be compatible with the rest of the codebase,
# we are using the new calibration method for so101 and so100
robot_type = "so_new_calibration"
kinematics = RobotKinematics(robot_type=robot_type)
kinematics = RobotKinematics(cfg.robot.urdf_path, cfg.robot.target_frame_name)
# Initialize min/max values
observation = robot.get_observation()
joint_positions = np.array([observation[f"{key}.pos"] for key in robot.bus.motors])
ee_pos = kinematics.forward_kinematics(joint_positions, frame="gripper_tip")[:3, 3]
ee_pos = kinematics.forward_kinematics(joint_positions)[:3, 3]
max_pos = joint_positions.copy()
min_pos = joint_positions.copy()
@@ -94,7 +95,7 @@ def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig):
observation = robot.get_observation()
joint_positions = np.array([observation[f"{key}.pos"] for key in robot.bus.motors])
ee_pos = kinematics.forward_kinematics(joint_positions, frame="gripper_tip")[:3, 3]
ee_pos = kinematics.forward_kinematics(joint_positions)[:3, 3]
# Skip initial warmup period
if (time.perf_counter() - start_episode_t) < 5:
@@ -113,6 +114,8 @@ def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig):
print(f"Min joint pos position {np.round(min_pos, 4).tolist()}")
break
busy_wait(0.01)
if __name__ == "__main__":
find_joint_and_ee_bounds()

9
src/lerobot/scripts/rl/crop_dataset_roi.py
View File

@@ -21,8 +21,7 @@ from pathlib import Path
from typing import Dict, Tuple
import cv2
# import torch.nn.functional as F # noqa: N812
import torch
import torchvision.transforms.functional as F # type: ignore # noqa: N812
from tqdm import tqdm # type: ignore
@@ -224,7 +223,8 @@ def convert_lerobot_dataset_to_cropper_lerobot_dataset(
cropped = F.crop(value, top, left, height, width)
value = F.resize(cropped, resize_size)
value = value.clamp(0, 1)
if key.startswith("complementary_info") and isinstance(value, torch.Tensor) and value.dim() == 0:
value = value.unsqueeze(0)
new_frame[key] = value
new_frame["task"] = task
@@ -266,8 +266,7 @@ if __name__ == "__main__":
)
parser.add_argument(
"--push-to-hub",
type=bool,
default=False,
action="store_true",
help="Whether to push the new dataset to the hub.",
)
parser.add_argument(

82
src/lerobot/scripts/rl/gym_manipulator.py
View File

@@ -254,20 +254,19 @@ class RobotEnv(gym.Env):
self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors]
self._image_keys = self.robot.cameras.keys()
# Read initial joint positions using the bus
self.current_joint_positions = self._get_observation()["agent_pos"]
self.current_observation = None
self.use_gripper = use_gripper
self._setup_spaces()
def _get_observation(self) -> np.ndarray:
def _get_observation(self) -> dict[str, np.ndarray]:
"""Helper to convert a dictionary from bus.sync_read to an ordered numpy array."""
obs_dict = self.robot.get_observation()
joint_positions = np.array([obs_dict[name] for name in self._joint_names], dtype=np.float32)
joint_positions = np.array([obs_dict[name] for name in self._joint_names])
images = {key: obs_dict[key] for key in self._image_keys}
return {"agent_pos": joint_positions, "pixels": images}
self.current_observation = {"agent_pos": joint_positions, "pixels": images}
def _setup_spaces(self):
"""
@@ -281,24 +280,24 @@ class RobotEnv(gym.Env):
- The action space is defined as a Box space representing joint position commands. It is defined as relative (delta)
or absolute, based on the configuration.
"""
example_obs = self._get_observation()
self._get_observation()
observation_spaces = {}
# Define observation spaces for images and other states.
if "pixels" in example_obs:
prefix = "observation.images" if len(example_obs["pixels"]) > 1 else "observation.image"
if "pixels" in self.current_observation:
prefix = "observation.images"
observation_spaces = {
f"{prefix}.{key}": gym.spaces.Box(
low=0, high=255, shape=example_obs["pixels"][key].shape, dtype=np.uint8
low=0, high=255, shape=self.current_observation["pixels"][key].shape, dtype=np.uint8
)
for key in example_obs["pixels"]
for key in self.current_observation["pixels"]
}
observation_spaces["observation.state"] = gym.spaces.Box(
low=0,
high=10,
shape=example_obs["agent_pos"].shape,
shape=self.current_observation["agent_pos"].shape,
dtype=np.float32,
)
@@ -340,14 +339,12 @@ class RobotEnv(gym.Env):
self.robot.reset()
# Capture the initial observation.
observation = self._get_observation()
# Reset episode tracking variables.
self.current_step = 0
self.episode_data = None
return observation, {"is_intervention": False}
self.current_observation = None
self._get_observation()
return self.current_observation, {"is_intervention": False}
def step(self, action) -> tuple[dict[str, np.ndarray], float, bool, bool, dict[str, Any]]:
"""
@@ -367,8 +364,6 @@ class RobotEnv(gym.Env):
- truncated (bool): True if the episode was truncated (e.g., time constraints).
- info (dict): Additional debugging information including intervention status.
"""
self.current_joint_positions = self._get_observation()["agent_pos"]
action_dict = {"delta_x": action[0], "delta_y": action[1], "delta_z": action[2]}
# 1.0 action corresponds to no-op action
@@ -376,6 +371,8 @@ class RobotEnv(gym.Env):
self.robot.send_action(action_dict)
self._get_observation()
if self.display_cameras:
self.render()
@@ -386,7 +383,7 @@ class RobotEnv(gym.Env):
truncated = False
return (
self._get_observation(),
self.current_observation,
reward,
terminated,
truncated,
@@ -399,11 +396,10 @@ class RobotEnv(gym.Env):
"""
import cv2
observation = self._get_observation()
image_keys = [key for key in observation if "image" in key]
image_keys = [key for key in self.current_observation if "image" in key]
for key in image_keys:
cv2.imshow(key, cv2.cvtColor(observation[key].numpy(), cv2.COLOR_RGB2BGR))
cv2.imshow(key, cv2.cvtColor(self.current_observation[key].numpy(), cv2.COLOR_RGB2BGR))
cv2.waitKey(1)
def close(self):
@@ -520,7 +516,10 @@ class AddCurrentToObservation(gym.ObservationWrapper):
Returns:
The modified observation with current values.
"""
present_current_observation = self.unwrapped._get_observation()["agent_pos"]
present_current_dict = self.env.unwrapped.robot.bus.sync_read("Present_Current")
present_current_observation = np.array(
[present_current_dict[name] for name in self.env.unwrapped.robot.bus.motors]
)
observation["agent_pos"] = np.concatenate(
[observation["agent_pos"], present_current_observation], axis=-1
)
@@ -1090,13 +1089,10 @@ class EEObservationWrapper(gym.ObservationWrapper):
dtype=np.float32,
)
# Initialize kinematics instance for the appropriate robot type
robot_type = getattr(env.unwrapped.robot.config, "robot_type", "so101")
if "so100" in robot_type or "so101" in robot_type:
# Note to be compatible with the rest of the codebase,
# we are using the new calibration method for so101 and so100
robot_type = "so_new_calibration"
self.kinematics = RobotKinematics(robot_type)
self.kinematics = RobotKinematics(
urdf_path=env.unwrapped.robot.config.urdf_path,
target_frame_name=env.unwrapped.robot.config.target_frame_name,
)
def observation(self, observation):
"""
@@ -1108,9 +1104,9 @@ class EEObservationWrapper(gym.ObservationWrapper):
Returns:
Enhanced observation with end-effector pose information.
"""
current_joint_pos = self.unwrapped._get_observation()["agent_pos"]
current_joint_pos = self.unwrapped.current_observation["agent_pos"]
current_ee_pos = self.kinematics.forward_kinematics(current_joint_pos, frame="gripper_tip")[:3, 3]
current_ee_pos = self.kinematics.forward_kinematics(current_joint_pos)[:3, 3]
observation["agent_pos"] = np.concatenate([observation["agent_pos"], current_ee_pos], -1)
return observation
@@ -1157,12 +1153,10 @@ class BaseLeaderControlWrapper(gym.Wrapper):
self.event_lock = Lock() # Thread-safe access to events
# Initialize robot control
robot_type = getattr(env.unwrapped.robot.config, "robot_type", "so101")
if "so100" in robot_type or "so101" in robot_type:
# Note to be compatible with the rest of the codebase,
# we are using the new calibration method for so101 and so100
robot_type = "so_new_calibration"
self.kinematics = RobotKinematics(robot_type)
self.kinematics = RobotKinematics(
urdf_path=env.unwrapped.robot.config.urdf_path,
target_frame_name=env.unwrapped.robot.config.target_frame_name,
)
self.leader_torque_enabled = True
self.prev_leader_gripper = None
@@ -1260,14 +1254,14 @@ class BaseLeaderControlWrapper(gym.Wrapper):
leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position")
follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position")
leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict], dtype=np.float32)
follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32)
leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict])
follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict])
self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - leader_pos[:-1]))
# [:3, 3] Last column of the transformation matrix corresponds to the xyz translation
leader_ee = self.kinematics.forward_kinematics(leader_pos, frame="gripper_tip")[:3, 3]
follower_ee = self.kinematics.forward_kinematics(follower_pos, frame="gripper_tip")[:3, 3]
leader_ee = self.kinematics.forward_kinematics(leader_pos)[:3, 3]
follower_ee = self.kinematics.forward_kinematics(follower_pos)[:3, 3]
action = np.clip(leader_ee - follower_ee, -self.end_effector_step_sizes, self.end_effector_step_sizes)
# Normalize the action to the range [-1, 1]
@@ -1341,6 +1335,9 @@ class BaseLeaderControlWrapper(gym.Wrapper):
# NOTE:
obs, reward, terminated, truncated, info = self.env.step(action)
if isinstance(action, np.ndarray):
action = torch.from_numpy(action)
# Add intervention info
info["is_intervention"] = is_intervention
info["action_intervention"] = action
@@ -1877,7 +1874,6 @@ def make_robot_env(cfg: EnvConfig) -> gym.Env:
if cfg.robot is None:
raise ValueError("RobotConfig (cfg.robot) must be provided for gym_manipulator environment.")
robot = make_robot_from_config(cfg.robot)
teleop_device = make_teleoperator_from_config(cfg.teleop)
teleop_device.connect()