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

2026-08-12

AI Summary

This study investigates how the quality of slot representations in object-centric world models influences planning performance and out-of-distribution generalization, clarifying the role of their inductive biases. Through controlled visual model-predictive control experiments, the authors evaluate object-centric models against scene-centric baselines along two axes: representation quality and robustness to distribution shifts, introducing unsupervised slot-quality metrics (FG-ARI and mBO). Their findings reveal a positive correlation—subject to saturation—between slot quality and planning success; high-quality slots eliminate reliance on proprioceptive or mask-based biases. Notably, an object-centric model leveraging frozen DINO pretrained features (DINO-WM) achieves substantially improved planning performance and out-of-distribution robustness, outperforming the end-to-end scene-centric LeWM model when slot binding is effective.

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.

Problem

• object-centric world models
• representation quality
• distribution shift
• planning robustness
• slot encoder

Innovation

• object-centric representation
• world models
• distribution shift robustness
• slot quality
• pretrained features