Open-World Law-to-Action: Vision-Language Priors and Touch for Parts with Hidden Physics

Abstract

A robot in a home it has never seen must open doors, drawers, lids and taps whose physics a picture does not show. Instructions such as "open the drawer halfway, gently" depend on that physics: how far a part travels, how it resists, and what locks it. We present Open-World Law-to-Action (L2A). A vision-language model (VLM) proposes, for each movable part, its motions, the terms of its law of motion, and what may lock it. Each proposal becomes a prior with a floor, so that touch can always overturn it. The robot identifies each part's law online by Bayesian linear regression with closed-form evidence, for parts that move in one way or in two, and keeps a hypothesis that an unseen part or motion locks the target. We prove that touch alone cannot rule this hypothesis out, derive when another look is worth its cost, and show that a constant load and friction cannot be told apart while a part moves one way. All experiments are in simulation, developed on some scenes and run once on held-out ones. In new kitchens the robot completes 80% of tasks from the VLM's proposals, against 98% when every part is known. Proposed physics raises success on objects that need new terms from 353 to 378-383 of 400 episodes, and a second look raises success in mixed scenes from 284 to 329 of 366 tasks. A mobile robot that takes nothing from the simulator completes 70 of 100 requests in new scenes, against 62 when the simulator says which part each box is (after one change made between two held-out tests).