chore: import upstream snapshot with attribution

2026-07-13 12:58:47 +08:00
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+# Backward compatibility
+
+## Policy Normalization Migration (PR #1452)
+
+**Breaking Change**: LeRobot policies no longer have built-in normalization layers embedded in their weights. Normalization is now handled by external `PolicyProcessorPipeline` components.
+
+### What changed?
+
+|                            | Before PR #1452                                  | After PR #1452                                               |
+| -------------------------- | ------------------------------------------------ | ------------------------------------------------------------ |
+| **Normalization Location** | Embedded in model weights (`normalize_inputs.*`) | External `PolicyProcessorPipeline` components                |
+| **Model State Dict**       | Contains normalization statistics                | **Clean weights only** - no normalization parameters         |
+| **Usage**                  | `policy(batch)` handles everything               | `preprocessor(batch)` → `policy(...)` → `postprocessor(...)` |
+
+### Impact on existing models
+
+- Models trained **before** PR #1452 have normalization embedded in their weights
+- These models need migration to work with the new `PolicyProcessorPipeline` system
+- The migration extracts normalization statistics and creates separate processor pipelines
+
+### Migrating old models
+
+Use the migration script to convert models with embedded normalization:
+
+```shell
+python src/lerobot/processor/migrate_policy_normalization.py \
+    --pretrained-path lerobot/act_aloha_sim_transfer_cube_human \
+    --push-to-hub \
+    --branch migrated
+```
+
+The script:
+
+1. **Extracts** normalization statistics from model weights
+2. **Creates** external preprocessor and postprocessor pipelines
+3. **Removes** normalization layers from model weights
+4. **Saves** clean model + processor pipelines
+5. **Pushes** to Hub with automatic PR creation
+
+### Using migrated models
+
+```python
+# New usage pattern (after migration)
+from lerobot.policies import make_policy, make_pre_post_processors
+
+# Load model and processors separately
+policy = make_policy(config, ds_meta=dataset.meta)
+preprocessor, postprocessor = make_pre_post_processors(
+    policy_cfg=config,
+    dataset_stats=dataset.meta.stats
+)
+
+# Process data through pipeline
+processed_batch = preprocessor(raw_batch)
+action = policy.select_action(processed_batch)
+final_action = postprocessor(action)
+```
+
+## Hardware API redesign
+
+PR [#777](https://github.com/huggingface/lerobot/pull/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.
+
+### 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            |
+
+---
+
+### 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.
+
+### 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.
+
+```diff
+   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"] -= 90
+```
+
+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 \
+    --robot.port=/dev/tty.usbmodem5A460814411 \
+    --robot.id=blue \
+   --robot.use_degrees=true \
+    --dataset.repo_id=my_dataset_id \
+    --dataset.episode=0
+```
+
+### Using policies trained with the previous calibration system
+
+Policies 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):
+
+```diff
+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](https://discord.gg/s3KuuzsPFb)