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