Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models

• URL: http://arxiv.org/abs/2608.12078v1
• Date: Wed, 12 Aug 2026 14:02:36 GMT
• Title: Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
• Abstract summary: We study object-centric representations for visual model-predictive control along two axes.<n>We find planning success correlates positively with unsupervised slot-quality metrics.<n>OCWM with well-bound slots is more robust overall than the end-to-end trained scene-centric LeWM.
• Score: 23.539029560904968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning world models from offline trajectories enables agents to accomplish different tasks through planning. Object-centric (OC) representations, which decompose a scene into a set of slots that bind to its objects, have been proposed as an inductive bias for world models that are more sample-efficient and generalize better. Yet prior object-centric world models (OCWMs) take the slot encoder as given and evaluate only in-distribution, leaving open whether the object-centric bias actually delivers for planning and what within the OCWM drives it. We conduct a controlled study of OCWMs for visual model-predictive control along two axes: object-centric representation quality and generalization under distribution shift relative to scene-centric models. We find that (i) planning success correlates positively with unsupervised slot-quality metrics (FG-ARI, mBO), though the gains saturate at high slot quality; (ii) with well-bound slots, the auxiliary proprioception inputs and masking inductive bias that prior methods relied on become unnecessary; and (iii) under unseen distribution shifts, the OCWM with well-bound slots is more robust overall than the end-to-end trained scene-centric LeWM, while DINO-WM, built on similar frozen pretrained features, remains competitive -- pointing to pretrained features as a key contributor to robustness.

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