# PhysVLMM: Interaction-Conditioned Physical Belief Maps for Articulated Objects in Robot Scene Maps

**Authors:** Dibyendu Ghosh  
**Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40539) · [PDF](https://arxiv.org/pdf/2610.40539) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40539.html)

## Key numbers

- **86%**: it identifies the joint type in 86% of held-out pulls
- **48%**: 48% for a learned single-view method
- **41%**: and predicts the response to a pull with 41% lower error than differentiable simulation

## Abstract

Mobile robots must open doors and drawers, yet their scene maps record what each object is and whether it has moved, not how it will respond to a pull. This paper proposes PhysVLMM, a method that maintains a physical belief for every articulated part of a Vision-Language-Motion Map and answers a planner's queries about it, with two algorithms: a map update and a query. Watching a part move provably cannot identify its dynamics, and a measured pull identifies only the pulled part, up to one direction. PhysVLMM therefore (i) keeps that direction as a posterior instead of a point estimate, (ii) passes the evidence of each pull in closed form to the untouched look-alike parts of the map, and (iii) answers whether an action will succeed at a stated risk, or abstains. On real doors and drawers in 18 homes, it identifies the joint type in 86% of held-out pulls (48% for a learned single-view method) and predicts the response to a pull with 41% lower error than differentiable simulation (CRPS 0.323 vs. 0.544), four orders of magnitude faster (13 ms vs. 230 s per case). The posterior lowers the final-position error from 0.460 to 0.270, sharing lowers the force error of untouched parts from 56% to 42%, and at every stated risk from 0.02 to 0.30 the false-safe answers stay within it. Pooling all measured parts of a home does as well as the look-alike groups, and simpler variants predict where a pulled part stops at least as well.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.