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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.
