Motion Belief Action: From a Visual Scene Map to Physical Decisions
Motion 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.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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kitchens
9
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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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17
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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
Open source S4
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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
Open source S3
arXiv:2611.12113v1 Announce Type: new Abstract: A robot that works among people must judge what it cannot see: whether an object is still where it was last seen, and whether a pull on a door or drawer is worth its risk. Maps that track object change hold nothing about force, maps of physical properties answer a risk question at a fixed risk level, and vision-language-action models keep no explicit estimate of hidden
Open source S2
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
Open source S4
tion, with the force limit at the training parts' median force, MBA raises SmolVLA's and Octo's scores by declining pulls; most declined door pulls would fail, but on drawers more would have opened, and a part-type average gets about half of this gain. In both, a published risk rule set to the risk these costs imply is MBA's rule for a single pull and decides as well in the main settings. A link between opening and h
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org