More Structure, Not More Capacity: Object-Centric Representations for Visuomotor Imitation Learning
Robotic manipulation policies rely on pre-trained vision models that give either a global scene embedding or a dense patch grid. Both mix task-relevant and task-irrelevant features. Object-centric slot representations are a structured alternative: they group features into a few per-object slots. We test what this structure buys on ManiSkill3 PickCube-v1, with a frozen encoder and a held-out-seed evaluation. Holding the policy, goal token, rendering, and calibration fixed and changing only the encoder, a frozen object-centric SPOT representation (DINO ViT-B/16 + Slot Attention) reaches 55.0$\pm$2.9% success, 22.4% above a dense DINO global-feature baseline (32.6 $\pm$ 1.5%), with the same ...
| Authors | Yi Li, Alexandre Chapin, Liming Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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