Motion Belief Action: From a Visual Scene Map to Physical Decisions
Overview
This research presents Motion Belief Action (MBA), a novel decision layer that enables robots to make physical decisions based on visual scene maps by estimating object permanence, force requirements, and risk levels. Evaluated across nine real kitchens and seventeen locations for door and drawer manipulation, MBA outperforms existing models like FreMEn and Perpetua in predicting object placement and deciding when to re-observe scenes. In simulation, MBA improves the performance of SmolVLA and Octo by declining risky pulls that would likely fail. The study also finds that certain proposed enhancements, such as linking opening and holding force or using probing nudges, do not consistently improve performance in the main experimental settings.
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Reported quantity · kitchens: 9 other · reported trials
“physics. We propose Motion Belief Action (MBA), a decision layer on the scene map. From how often people move an object and how much force it needs, how much load it holds and whether it is blocked, MBA chooses under a force limit to look again, nudge, open, brace or decline, with its risk set by the task's costs. On real activity in 9 kitchens, it predicts whether an object is still in place better than FreMEn and P”
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Reported quantity · locations: 17 other · reported trials
“erpetua, and decides when to look again at lower cost than these and the persistence filter, because about a quarter of placements end in a pick-up within 30 s. On real pulls of doors and drawers in 17 locations, made by a person with a force-sensing gripper, the map's force estimates decide better than a part-type average. Setting the risk from the costs also beats a published risk rule at its fixed level. In simula”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalMotion Belief Action: From a Visual Scene Map to Physical Decisions
This research presents Motion Belief Action (MBA), a novel decision layer that enables robots to make physical decisions based on visual scene maps by estimating object permanence, force requirements, and risk levels. Evaluated across nine real kitchens and seventeen locations for door and drawer manipulation, MBA outperforms existing models like FreMEn and Perpetua in predicting object placement and deciding when to re-observe scenes. In simulation, MBA improves the performance of SmolVLA and Octo by declining risky pulls that would likely fail. The study also finds that certain proposed enhancements, such as linking opening and holding force or using probing nudges, do not consistently improve performance in the main experimental settings.
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