Object-Centric Representations Significantly Improve Visuomotor Imitation Learning in Robotics

Jul 14, 2026

A new preprint demonstrates that object-centric slot representations, such as SPOT (DINO ViT-B/16 + Slot Attention), substantially improve robotic manipulation success rates in imitation learning tasks compared to dense global features or patch grids. On the ManiSkill3 PickCube-v1 benchmark, a frozen SPOT encoder achieved a 55% success rate, outperforming a dense baseline by 22.4 percentage points, without increasing model capacity or requiring encoder fine-tuning. Further gains were observed by adding explicit spatial goal information and higher-resolution rendering.

Why it matters: This work highlights that structured object-centric representations can meaningfully enhance visuomotor imitation learning for robotic manipulation, offering a practical advance without increasing model complexity.

Full story at: arXiv Robotics ↗