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adding instructions for different embodiement + fixing some tests

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Maximellerbach
2026-05-26 11:51:54 +02:00
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docs/source/policy_vla_jepa_README.md
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@@ -66,15 +66,16 @@ All checkpoints use `Qwen/Qwen3-VL-2B-Instruct` as the language backbone.
Key parameters in `VLAJEPAConfig`:
| Parameter | Default | Description |
| ------------------------- | ------- | -------------------------------------------------------------- |
| `chunk_size` | 7 | Number of actions predicted per inference call |
| `n_action_steps` | 7 | Steps executed from the predicted chunk before re-planning |
| `num_video_frames` | 8 | Video clip length fed to the world model |
| `enable_world_model` | `True` | Whether to load and train the V-JEPA2 predictor |
| `world_model_loss_weight` | 0.1 | Weight of the JEPA prediction loss relative to the action loss |
| `num_inference_timesteps` | 4 | Euler integration steps for action denoising |
| `freeze_qwen` | `False` | Freeze the Qwen3-VL backbone and only train the action head |
| Parameter | Default | Description |
| ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `chunk_size` | 7 | Number of actions predicted per inference call |
| `n_action_steps` | 7 | Steps executed from the predicted chunk before re-planning |
| `num_video_frames` | 8 | Video clip length fed to the world model |
| `enable_world_model` | `True` | Whether to load and train the V-JEPA2 predictor |
| `world_model_loss_weight` | 0.1 | Weight of the JEPA prediction loss relative to the action loss |
| `num_inference_timesteps` | 4 | Euler integration steps for action denoising |
| `freeze_qwen` | `False` | Freeze the Qwen3-VL backbone and only train the action head |
| `reinit_modules` | `None` | Key prefixes allowed to be randomly re-initialised on load (for cross-embodiment transfer, see [Fine-tuning on a different embodiment](#fine-tuning-on-a-different-embodiment)) |
---
@@ -110,6 +111,29 @@ lerobot-train \
--dataset.repo_id=your_org/your_dataset
```
### Fine-tuning on a different embodiment
When the target robot has a different action or state dimensionality than the pretrained checkpoint, the input/output projection layers of the action head will have mismatched shapes and cannot be loaded directly. `reinit_modules` lets you list the key prefixes that are allowed to mismatch — those layers are randomly re-initialised while every other weight is reused from the checkpoint. Any shape mismatch outside the listed prefixes raises an error.
The layers that depend on `action_dim` and `state_dim` are:
| Layer | Key prefix |
| ----------------------------------------- | ----------------------------------- |
| Action encoder (action_dim → inner_dim) | `model.action_model.action_encoder` |
| Action decoder (hidden_size → action_dim) | `model.action_model.action_decoder` |
| State encoder (state_dim → inner_dim) | `model.action_model.state_encoder` |
```bash
lerobot-train \
--policy.path=lerobot/VLA-JEPA-Pretrain \
--policy.repo_id=your_org/your_repo \
--policy.freeze_qwen=true \
--policy.reinit_modules='["model.action_model.action_encoder", "model.action_model.action_decoder", "model.action_model.state_encoder"]' \
--dataset.repo_id=your_org/your_dataset
```
If your robot has no proprioceptive state, omit `model.action_model.state_encoder` from the list.
### Reproducing the LIBERO results
**Training on LIBERO:**
@@ -132,7 +156,7 @@ lerobot-eval \
--env.type=libero \
--env.task=libero_spatial,libero_object,libero_goal,libero_10 \
--eval.n_episodes=10 \
--eval.batch_size=5 \
--eval.batch_size=5
```
@@ -145,9 +169,19 @@ lerobot-eval \
--env.task=libero_10 \
--env.task_ids='[0,1,2]' \
--eval.n_episodes=10 \
--eval.batch_size=5 \
--eval.batch_size=5
```
**Expected results:**
| Suite | Episodes | Successes | Success Rate |
| -------------- | -------- | --------- | ------------ |
| libero_spatial | 100 | 93 | **95.0%** |
| libero_object | 100 | 100 | **100.0%** |
| libero_goal | 100 | 98 | **98.0%** |
| libero_10 | 100 | 96 | **93.0%** |
| **Overall** | **400** | **387** | **96.5%** |
---
## Fine-tuning on single-camera datasets