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 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 Perpetua, 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 simulation, 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 holding force, a probing nudge and an action prior for camera tracking do not help in the main settings, and over the real-pull sweep the nudge costs a little.

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