Object-Centric Representations Improve Policy Generalization in Robot
  Manipulation
Authors: name: Alexandre Chapin, name: Bruno Machado, name: Emmanuel Dellandrea, name: Liming Chen

Visual representations are central to the learning and generalization
capabilities of robotic manipulation policies. While existing methods rely on
global or dense features, such representations often entangle task-relevant and
irrelevant scene information, limiting robustness under distribution shifts. In
this work, we investigate object-centric representations (OCR) as a structured
alternative that segments visual input into a finished set of entities,
introducing inductive biases that align more naturally with manipulation tasks.
We benchmark a range of visual encoders-object-centric, global and dense
methods-across a suite of simulated and real-world manipulation tasks ranging
from simple to complex, and evaluate their generalization under diverse visual
conditions including changes in lighting, texture, and the presence of
distractors. Our findings reveal that OCR-based policies outperform dense and
global representations in generalization settings, even without task-specific
pretraining. These insights suggest that OCR is a promising direction for
designing visual systems that generalize effectively in dynamic, real-world
robotic environments.