| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| library_name: pytorch |
| tags: |
| - robotics |
| - vision-language-action |
| - robot-planning |
| - 3d-reconstruction |
| - long-term-memory |
| - training-free |
| --- |
| |
| # GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning |
|
|
| GeneralVLA-2 is a training-free framework that strengthens robot planning with two complementary components: |
|
|
| 1. **GeoFuse-MV3D** produces conservative, mask-verified and appearance-preserving object geometry from calibrated multi-view observations. |
| 2. **Governed KnowledgeBank** provides quality-, confidence-, lifecycle-, conflict- and geometry-aware experience retrieval. |
|
|
| GeneralVLA-2 does not introduce a newly trained monolithic checkpoint. This repository bundles selected runtime weights and representative GeoFuse-MV3D 3D Gaussian reconstruction outputs so that the commonly used vision and grasping components can be obtained from one repository. |
|
|
| ## Repository contents |
|
|
| - `weights/sam/sam_vit_h_4b8939.pth`: SAM ViT-H segmentation checkpoint. |
| - `weights/clip-vit-large-patch14/`: CLIP ViT-L/14 vision-language encoder and tokenizer files. |
| - `weights/graspnet/checkpoint-rs.tar`: GraspNet checkpoint used by the robot stack. |
| - `geofuse_mv3d/examples/alarm_refined.ply`: refined 3D Gaussian reconstruction for the alarm scene. |
| - `geofuse_mv3d/examples/shoe_refined.ply`: refined 3D Gaussian reconstruction for the shoe scene. |
| - `generalvla2.yaml`: compact deployment-oriented configuration. |
| - `asset_manifest.json`: machine-readable description of the bundled assets. |
|
|
| The PLY files are method outputs rather than neural-network checkpoints. They contain position, appearance, opacity, scale and rotation attributes for the reconstructed 3D Gaussians. |
|
|
| ## Download |
|
|
| ```bash |
| hf download AIGeeksGroup/GeneralVLA-2 \ |
| --local-dir vendor/GeneralVLA-2 |
| ``` |
|
|
| ## Code and project page |
|
|
| - Code: https://github.com/AIGeeksGroup/GeneralVLA-2 |
| - Project page: https://aigeeksgroup.github.io/GeneralVLA-2 |
| - Paper: https://arxiv.org/abs/2606.17480 |
|
|
| ## Reported results |
|
|
| - On GSO-30, GeoFuse-MV3D reduces CD and LPIPS by 2.20% and 2.02%, while increasing PSNR and SSIM by 2.36% and 1.03% over the MV-SAM3D baseline under the same input-view protocol. |
| - Governed KnowledgeBank improves over ReasoningBank by 4.53% on Terminal-Bench 2.0 success rate and 3.73% on SWE-Bench Verified resolve rate, while reducing average steps by 4.95% and 5.65%. |
| - GeneralVLA-2 produces successful trajectories on all 14 evaluated RLBench tasks without parameter training and is best among the compared methods on 10 tasks. |
|
|
| ## License notice |
|
|
| The Apache-2.0 metadata applies to the GeneralVLA-2 configuration and documentation. Bundled third-party checkpoints retain their respective upstream licenses and terms. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2026generalvla2, |
| title={GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning}, |
| author={Wang, Haoyu and Ma, Guoqing and Zhang, Zeyu and Guo, Yandong and Shi, Boxin and Tang, Hao}, |
| journal={arXiv preprint arXiv:2606.17480}, |
| year={2026} |
| } |
| ``` |
|
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|
