tangger/lerobot
1
0
Fork 1
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Update pre-commit-config.yaml + pyproject.toml + ceil rerun & transformer dependencies version (#1520)

Browse Source 
* chore: update .gitignore

* chore: update pre-commit

* chore(deps): update pyproject

* fix(ci): multiple fixes

* chore: pre-commit apply

* chore: address review comments

* Update pyproject.toml

Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

* chore(deps): add todo

---------

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Ben Zhang <5977478+ben-z@users.noreply.github.com>
This commit is contained in:
Steven Palma
2025-07-17 14:30:20 +02:00
committed by GitHub
parent 0938a1d816
commit 378e1f0338
78 changed files with 1450 additions and 636 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
docs/source/backwardcomp.mdx
View File

@@ -6,21 +6,22 @@ PR [#777](https://github.com/huggingface/lerobot/pull/777) improves the LeRobot
### What changed?
| | 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 |
| | 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 |
---
### Impact on existing datasets
* Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
* Joint angles are offset and incorrectly normalized.
* Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
- Recorded trajectories created **before** PR #777 will replay incorrectly if loaded directly:
- Joint angles are offset and incorrectly normalized.
- Any models directly finetuned or trained on the old data will need their inputs and outputs converted.
### Using datasets made with the previous calibration system
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.
@@ -33,20 +34,31 @@ Below we take you through the modifications that are done in the example script
Let's break this down.
New codebase uses `.pos` suffix for the position observations and we have removed `main_` prefix:
<!-- prettier-ignore-start -->
```python
key = f"{name.removeprefix('main_')}.pos"
```
<!-- prettier-ignore-end -->
For `"shoulder_lift"` (id = 2), the 0 position is changed by -90 degrees and the direction is reversed compared to old calibration/code.
<!-- prettier-ignore-start -->
```python
action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
```
<!-- prettier-ignore-end -->
For `"elbow_flex"` (id = 3), the 0 position is changed by -90 degrees compared to old calibration/code.
<!-- prettier-ignore-start -->
```python
action["elbow_flex.pos"] -= 90
```
<!-- prettier-ignore-end -->
To use degrees normalization we then set the `--robot.use_degrees` option to `true`.
```diff
python examples/backward_compatibility/replay.py \
--robot.type=so101_follower \
@@ -63,6 +75,7 @@ Policies output actions in the same format as the datasets (`torch.Tensors`). Th
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):
```diff
action_values = predict_action(
observation_frame,