| --- |
| license: apache-2.0 |
| tags: |
| - robotics |
| - manipulation |
| - grasp |
| - lerobot |
| - clip |
| --- |
| |
| # A2 Pretrained Policy |
|
|
| Pretrained ViLGP3D policy for 6-DOF grasp and place tasks in tabletop manipulation. |
|
|
| ## Model Description |
|
|
| This model uses CLIP-based cross-attention for selecting grasp and place poses from candidates generated by GraspNet/PlaceNet. |
|
|
| ## Files |
|
|
| - `sl_checkpoint_199.pth`: Trained policy weights (ViLGP3D fusion network) |
| - `checkpoint-rs.tar`: GraspNet checkpoint for grasp candidate generation |
|
|
| ## Usage |
|
|
| ### With lerobot_policy_a2 |
|
|
| ```python |
| from lerobot_policy_a2 import A2Policy |
| |
| # Load pretrained model |
| policy = A2Policy.from_pretrained("dgrachev/a2_pretrained") |
| |
| # Use for grasp prediction |
| action, info = policy.predict_grasp( |
| color_images={"front": rgb_image}, |
| depth_images={"front": depth_image}, |
| point_cloud=point_cloud, |
| lang_goal="grasp a round object" |
| ) |
| ``` |
|
|
|
|
| ## Training Details |
|
|
| - **Architecture**: ViLGP3D with CLIP ViT-B/32 backbone |
| - **Hidden dim**: 768 |
| - **Attention heads**: 8 |
| - **Position encoding**: Rotary Position Encoding (RoPE) |
| - **Training data**: Tabletop manipulation demonstrations |
|
|
| ## Related Resources |
|
|
| - [lerobot_policy_a2](https://github.com/dgrachev/lerobot_policy_a2) - Policy package |
| - [lerobot_grach0v](https://github.com/grach0v/lerobot) - LeRobot fork with A2 environment |
| - [a2_assets](https://huggingface.co/datasets/dgrachev/a2_assets) - Environment assets |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{a2_policy, |
| author = {Denis Grachev}, |
| title = {A2 Policy: CLIP-based 6-DOF Grasp and Place Policy}, |
| year = {2025}, |
| publisher = {HuggingFace}, |
| url = {https://huggingface.co/dgrachev/a2_pretrained} |
| } |
| ``` |
|
|
