3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also anticipating where the interaction is heading. Existing policies largely leave such foresight to emerge implicitly from act...
Zhong-Bo Zhang, Zai-Bin Zhang, Yi-Fan Wang et al.· 0 citations
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.
Chang-Bo Yan, Zhong-Bo Zhang, Zai-Bin Zhang et al.· 0 citations
This work proposes CARE (Corrective Atomic Robotic Execution), a framework that improves recovery by learning from failures encountered during execution, and introduces the Failure State Recovery Benchmark (FSR-Bench), which evaluates recovery from intermediate failure states under local deviations and structural anoma...
Jun-Lan Xiao, Jun-Wei Jiang, Zai-Bin Zhang et al.· 0 citations
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.
Zhong-Bo Zhang, Jia-Yi Jin, Yi-Fan Wang et al.· 1 citation
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