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
3D point-cloud observations are inherently ambiguous in complex, cluttered manipulation scenes, where target objects may be partially occluded or tightly intermingled with visually similar distractors. As a result, standard 3D diffusion policies often struggle to localize and exploit task-relevant geometry as scene com...
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.· 0 citations
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