Identifying Habit, Physics, and Nuisance in Robot World Models

Abstract:Teleoperated demonstrations are often multimodal even when the underlying dynamics are nearly deterministic given the executed action. We argue that this multimodality typically mixes three factors--operator habit in action selection, shared physics, and observation nuisance--and that entangled next-observation predictors absorb all three. We formalize the split with a structural causal model a=g(h,z,u), z'=f(z,a), o=r(z,c), and test it with complementary interventions: replacing or shuffling actions at fixed state sharply increases next-state error, whereas appearance and camera changes should not; habit-aware reverse scoring improves ranking of feasible pasts without rewriting the dynamics. The associated adaptation rule is to freeze a shared physics readout and update only a thin interface. On StackCube, DROID, and RH20T this rule improves low-shot transfer relative to training from scratch, retains cleaner dynamics under corrupted adaptation data, and extends from proprioception to pixel observations with multi-view and multi-step checks. We do not equate latent actions with operator habit, and we do not target large-scale video generation benchmarks.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.09210 [cs.RO]

(or arXiv:2609.09210v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.09210 Focus to learn more arXiv-issued DOI via DataCite

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