Object-Centric Representations Improve Policy Generalization in Robot
  Manipulation
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.
682db967a9a344c017078ee3
Alexandre Chapin
claimed_verified
2025-05-21T12:26:48.791Z
63ba99e3d90985e7acd820d8
https://cdn-avatars.huggingface.co/v1/production/uploads/63ba99e3d90985e7acd820d8/9Ywt1MY9UBdmlnVuuSIm-.jpeg
Alexandre Chapin
user
Beegbrain
682db967a9a344c017078ee4
Bruno Machado
682db967a9a344c017078ee5
Emmanuel Dellandrea
682db967a9a344c017078ee6
Liming Chen
Object-Centric Representations Improve Policy Generalization in Robot
  Manipulation
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.
Object-centric representations (OCR) demonstrate superior generalization in robotic manipulation tasks compared to global or dense visual features under various visual conditions.
object-centric representations OCR visual encoders global features dense features manipulation tasks generalization distribution shifts visual conditions lighting changes texture changes distractors