This is the official repository for the paper:
GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning
Haoyu Wang*, Guoqing Ma*, Zeyu Zhang*†, Yandong Guo, Boxin Shi, and Hao Tang#
*Equal contribution. †Project lead. #Corresponding author.
This repository contains three code components used in the GeneralVLA project:
GeneralVLA/: robot memory VLA runtime, model configuration, evaluation tools, and tests.GeoFuse-MV3D/: multi-view 3D geometry fusion and evaluation utilities.KnowledgeBank/: memory-augmented software-agent code and benchmark scripts.
Large checkpoints, datasets, generated outputs, robot logs, and benchmark trajectories are intentionally not included. Each component documents its own setup steps and expected external assets.
@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}
}
.
├── GeneralVLA/
├── GeoFuse-MV3D/
├── KnowledgeBank/
├── THIRD_PARTY_NOTICES.md
└── README.md
Install and run each component from its own directory:
cd GeneralVLA
bash scripts/bootstrap.sh
pytest -qcd GeoFuse-MV3D
pip install -r requirements.txt
python scripts/run_full_pipeline.py --config configs/paths.local.yamlcd KnowledgeBank/third_party
pip install -e .
pytest tests -qSee the README inside each subdirectory for detailed asset paths, model configuration, and benchmark-specific instructions.
The repository is code-only. Before running the full pipelines, prepare the external assets described by each component, including model checkpoints, benchmark datasets, WebArena services, GSO assets, and robot/runtime-specific configuration.
Component asset entry points:
GeneralVLA/: project model assets are expected fromhttps://huggingface.co/AIGeeksGroup/GeneralVLA.GeoFuse-MV3D/: use the official upstream assets documented inGeoFuse-MV3D/docs/external_assets.md.KnowledgeBank/: use the official benchmark/model-provider assets documented inKnowledgeBank/README.md.
Do not commit API keys, model checkpoints, local datasets, generated results, or private trajectories.