Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots. A common remedy is to fine-tune these models on robot-specific demonstrations. However, robot data collection can be prohibitively expensive and time-consuming, which is particularly acute in dexterous manipulation, e.g., teleoperating a multi-fingered hand for even a single atomic task can take days. To address this, we introduce Dexterous Point Policy, a framework that learns dexterous manipulation policies directly from human videos and requires no robot demonstrations. Our core insight is that a unifie...
| Authors | Beomjun Kim, Seong Hyeon Park, Seunghoon Sim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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