GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning
Summary
GeneralVLA-2 introduces GeoFuse-MV3D for improved 3D reconstruction and a governed KnowledgeBank for better memory management in robotic manipulation tasks, achieving performance gains on several benchmarks.
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Paper page - GeneralVLA-2: Geometry-Aware Reconstruction and Governed Memory for Robot Planning
Source: https://huggingface.co/papers/2606.17480
Abstract
GeneralVLA-2 addresses limitations in vision-language-action systems by introducing GeoFuse-MV3D for improved 3D reconstruction and an enhanced KnowledgeBank for better memory management in robotic manipulation tasks.
Generalistvision-language-action systemsneed object-centric 3D evidence and reusable manipulation experience to plan reliable robot trajectories. GeneralVLA provides a hierarchical interface for converting language and RGB-D observations into 3D end-effector paths, but two bottlenecks remain. First, monocular SAM3D-style object reconstruction can hallucinate pose and unseen geometry, while manipulation benefits from stable object shape when calibrated multi-view observations are available. Second, the originalKnowledgeBankmainly retrieves semantically similar snippets and appends new knowledge, which makes it difficult to controlmemory quality, conflicts,confidence, and geometric relevance. To address the first challenge, we introduceGeoFuse-MV3D, a geometry-prior-guidedMV-SAM3Dreconstruction branch that verifies external geometry cues with input-view masks, applies softvisual-hull support, performsaxis-wise refinement, and fuses only geometry while preserving appearance. To address the second challenge, we upgradeKnowledgeBankinto a governed long-term memory system with explicit quality,confidence, lifecycle,verifier, and conflict metadata, together withprecision-oriented retrieval. Finally, we evaluate the reconstruction branch on GSO-30 and the memory module on Terminal-Bench 2.0 and SWE-Bench Verified;GeoFuse-MV3Dimproves over theMV-SAM3Dbaseline by reducing CD and LPIPS by 2.20% and 2.02% while increasing PSNR and SSIM by 2.36% and 1.03%, andKnowledgeBankimproves over ReasoningBank by 4.53% on Terminal-Bench SR and 3.73% on SWE-Bench resolve rate, while reducing AS by 4.95% and 5.65%, respectively. Code: https://github.com/AIGeeksGroup/GeneralVLA-2. Website: https://aigeeksgroup.github.io/GeneralVLA-2.
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