From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations
Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approaches leverage human video demonstrations to mitigate this shortage, they remain computationally expensive and still rely on paired human-robot data for domain alignment. Although current state-of-the-arts excel at long-horizon tasks, they struggle with the delicate and precise control required for complex tool manipulation. To overcome these limitations, we introduce P2P-T, from Pixel to Poses for Tool Manipulation, a data-efficient, object-centric framework that learns tool use directly from human demonstrations. P2P-T bridges the cognitive and physical execution gap thr...
| Authors | Bangjun Wang, Longyan Wu, Yukun Wei et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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