This is the LeRobot port of **VLA-JEPA**, a Vision-Language-Action model that combines a Qwen3-VL language backbone with a self-supervised video world model (V-JEPA2) and a flow-matching DiT action head.
| **Qwen3-VL backbone** | `Qwen3VLInterface` | Fuses images + language instruction into context tokens |
| **DiT-B action head** | `VLAJEPAActionHead` | Flow-matching diffusion over the action chunk |
| **V-JEPA2 world model** | `ActionConditionedVideoPredictor` | Self-supervised video prediction loss (training only) |
### Data flow
**Training:**
1. A video clip of `num_video_frames` frames is encoded by V-JEPA2 into per-frame patch tokens.
2. The Qwen3-VL backbone processes multi-view images + the task instruction and produces a sequence of context tokens that includes special action tokens (for world model conditioning) and embodied tokens.
3. The action head receives those context tokens as cross-attention keys/values and predicts a denoised action chunk via flow matching.
4. The world model predictor uses the action tokens extracted from Qwen to predict future V-JEPA2 frame embeddings; a regression loss on those predictions is added to the action loss.
**Inference:**
Only Qwen + the action head are used. The world model is not needed at inference time.
### Action head details
The action head is a **Diffusion Transformer (DiT-B)** with flow matching:
- **Output dim**: `action_hidden_size` (default 1024), projected down to `action_dim`
- **Cross/self alternation**: even-indexed DiT blocks attend to Qwen context tokens (cross-attention); odd-indexed blocks are self-attention
- **Noise schedule**: Beta distribution with parameters `action_noise_beta_alpha` / `action_noise_beta_beta`
- **Inference**: Euler integration over `num_inference_timesteps` steps
Available presets via `action_model_type`:
| Preset | Hidden dim | Heads | Head dim |
| ------- | ---------- | ----- | -------- |
| `DiT-B` | 768 | 12 | 64 |
| `DiT-L` | 1536 | 32 | 48 |
### World model details
The video predictor is a ViT-style transformer (`ActionConditionedVideoPredictor`) that takes:
- **Frame tokens**: V-JEPA2 patch embeddings projected to `predictor_embed_dim`
- **Action tokens**: Qwen action token embeddings projected to `predictor_embed_dim`
It uses block-causal attention so each temporal step can attend to all previous steps. The predictor's input `embed_dim` equals `num_views × video_encoder_hidden_size` (e.g. 2 views × 1024 = 2048 for the pretrained checkpoints).
---
## Pretrained Checkpoints
Three checkpoints are available, converted from [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA):
| Checkpoint | Dataset | Cameras | World model | Action dim |
\* The SimplerEnv checkpoint was fine-tuned from Pretrain. The world model predictor architecture expects `embed_dim=2048` (2-camera input) but SimplerEnv is single-camera, so the world model cannot be loaded cleanly. Since inference only needs Qwen + the action head, `enable_world_model=False` is set for this variant. See [Fine-tuning on single-camera datasets](#fine-tuning-on-single-camera-datasets) for implications.
All checkpoints use `Qwen/Qwen3-VL-2B-Instruct` as the language backbone.
### Loading a pretrained checkpoint
```python
from lerobot.policies.vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
- [ ] double check which LIBERO dataset (libero_10 or full libero) was used for training the checkpoint
- [ ] add the evaluation command for the pretrained checkpoint + check that the results match the original paper
```bash
lerobot-train \
policy.path=lerobot/VLA-JEPA-Pretrain \
dataset.repo_id=lerobot/libero_10 \
training.num_steps=50000 \
env.type=libero \
env.task=libero_10
```
**Evaluating the pretrained LIBERO-10 checkpoint:**
```bash
lerobot-eval \
--policy.path=lerobot/VLA-JEPA-LIBERO \
--env.type=libero \
--env.task=libero_10 \
--env.obs_type=pixels_agent_pos \
--eval.n_episodes=500 \
--eval.batch_size=10 \
--policy.device=cuda
```
This runs all 10 LIBERO-10 tasks (50 episodes each, 500 total) with the default camera setup (`agentview_image` → `observation.images.image`, `robot0_eye_in_hand_image` → `observation.images.image2`) and the `pixels_agent_pos` obs type that provides both images and robot state.
To evaluate a subset of tasks only:
```bash
lerobot-eval \
--policy.path=lerobot/VLA-JEPA-LIBERO \
--env.type=libero \
--env.task=libero_10 \
--env.task_ids='[0,1,2]' \
--eval.n_episodes=50 \
--eval.batch_size=5 \
--policy.device=cuda
```
---
## Fine-tuning on single-camera datasets
The pretrained world model predictor was trained with `embed_dim = num_views × 1024`. If your target dataset has fewer cameras than the source checkpoint, the predictor input projection will have a shape mismatch and cannot be loaded.
**Option 1 — Disable the world model (recommended)**
Set `enable_world_model=False`. Only the Qwen backbone and action head are loaded and trained. This matches the original SimplerEnv fine-tuning strategy and is sufficient for good action performance.
```bash
lerobot-train \
policy.path=lerobot/VLA-JEPA-Pretrain \
policy.enable_world_model=false \
dataset.repo_id=your_org/single_camera_dataset
```
**Option 2 — Reinitialize the predictor input projection**
If you want the JEPA self-supervised signal during fine-tuning, load the checkpoint with `strict=False` and reinitialize `model.video_predictor.predictor_embed` for the new `embed_dim`. All other predictor block weights (attention, MLP, norm, output projection) are camera-count-agnostic and can be reused from the pretrained checkpoint.
---
## Citation
```bibtex
@misc{vla_jepa_2025,
title = {VLA-JEPA: Vision-Language-Action Model with Joint-Embedding Predictive Architecture},
author = {Gin, Wind and others},
year = {2025},
url = {https://huggingface.co/ginwind/VLA-JEPA},
}
```
---
## License
Weights are distributed under the license terms of the original [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA) repository. The LeRobot integration code follows the **Apache 2.0 License**.
