Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation

arXiv:2601.22988v2 Announce Type: replace Abstract: Real-world robotic manipulation requires visuomotor policies capable of robust 3D scene reasoning under varying camera viewpoints. While recent 3D-aware manipulation policies have shown promise, they still face several limitations: (i) reliance on multi-view observations during inference, which is impractical in camera-constrained deployments; (ii) insufficient geometric fidelity in learned representations, limiting precise and viewpoint-robust control; and (iii) the lack of effective mechanisms for transferring pretrained 3D representations to downstream visuomotor policies. To address these challenges, we present GEM3D (Geometrically-Grounded 3D Manipulation), a unified 3D representation and policy learning framework for view-generalizable robotic manipulation. GEM3D learns amodal 3D representations from single-view RGB-D observations by jointly enforcing point-cloud reconstruction and Gaussian-splatting-based novel-view consistency during pretraining. A multi-step distillation strategy then transfers the learned geometric understanding into a deployable single-view visuomotor policy for downstream manipulation control. Experiments on 12 RLBench tasks and three real-world tasks demonstrate strong manipulation performance. Further zero-shot evaluations on eight RLBench tasks and the real-world tasks show robust generalization to unseen camera poses and substantial gains over existing methods.

Source: arXiv cs.RO | 2026-09-29