Zhong-Bo Zhang

The results show that a diffusion policy can benefit substantially from knowing where an interaction is heading, without being told exactly where to move.

VA-Bench is introduced to evaluate the complete observe-reason-act-revise loop and tests whether general-purpose MLLMs can turn visual demonstrations and actively acquired evidence into successful embodied action.

Attention-DP3 is proposed, a spatially object-aware 3D diffusion policy that injects object-level geometric cues via attention while keeping the DP3 diffusion backbone unchanged and shows consistent improvements over DP3, achieving state-of-the-art performance across benchmarks.

MA-VLA achieves robust multi-arm compositional generalization, outperforming existing models by effectively enabling role-agnostic behavior in unseen collaboration scenarios.