OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer
We present OCRA, an Object-Centric framework for video-based human-to-Robot Action transfer that learns directly from human demonstration videos to enable robust manipulation. Object-centric learning emphasizes task-relevant objects and their interactions while filtering out irrelevant background, providing a natural and scalable way to teach robots. OCRA leverages multi-view RGB videos, the state-of-the-art 3D foundation model VGGT, and advanced detection and segmentation models to reconstruct object-centric 3D point clouds, capturing rich interactions between objects. To handle properties not easily perceived by vision alone, we incorporate tactile priors via a large-scale dataset of ov...
| Authors | Kuanning Wang, Ke Fan, Yuqian Fu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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