PR #777 improves the LeRobot calibration but is not backward-compatible. Below is a overview of what changed and how you can continue to work with datasets created before this pull request.
| Before PR #777 | After PR #777 | |
|---|---|---|
| Joint range | Degrees -180...180° | Normalised range Joints: –100...100 Gripper: 0...100 |
| Zero position (SO100 / SO101) | Arm fully extended horizontally | In middle of the range for each joint |
| Boundary handling | Software safeguards to detect ±180 ° wrap-arounds | No wrap-around logic needed due to mid-range zero |
We provide a migration example script for replaying an episode recorded with the previous calibration here: examples/backward_compatibility/replay.py.
Below we take you through the modifications that are done in the example script to make the previous calibration datasets work.
+ key = f"{name.removeprefix('main_')}.pos"
action[key] = action_array[i].item()
+ action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
+ action["elbow_flex.pos"] -= 90Let’s break this down.
New codebase uses .pos suffix for the position observations and we have removed main_ prefix:
key = f"{name.removeprefix('main_')}.pos"For "shoulder_lift" (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)For "elbow_flex" (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
action["elbow_flex.pos"] -= 90To use degrees normalization we then set the --robot.use_degrees option to true.
python examples/backward_compatibility/replay.py \
--robot.type=so101_follower \
--robot.port=/dev/tty.usbmodem5A460814411 \
--robot.id=blue \
+ --robot.use_degrees=true \
--dataset.repo_id=my_dataset_id \
--dataset.episode=0Policies output actions in the same format as the datasets (torch.Tensors). Therefore, the same transformations should be applied.
To find these transformations, we recommend to first try and and replay an episode of the dataset your policy was trained on using the section above.
Then, add these same transformations on your inference script (shown here in the record.py script):
action_values = predict_action(
observation_frame,
policy,
get_safe_torch_device(policy.config.device),
policy.config.use_amp,
task=single_task,
robot_type=robot.robot_type,
)
action = {key: action_values[i].item() for i, key in enumerate(robot.action_features)}
+ action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
+ action["elbow_flex.pos"] -= 90
robot.send_action(action)If you have questions or run into migration issues, feel free to ask them on Discord
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