[Submitted on 6 Sep 2026]
Title:Identifying Habit, Physics, and Nuisance in Robot World Models
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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.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09210 [cs.RO]
(or arXiv:2609.09210v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.09210
arXiv-issued DOI via DataCite
Submission history
From: Jinting Hang [view email] [v1] Sun, 6 Sep 2026 00:20:22 UTC (205 KB)
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