DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely from demonstrations often suffer from compounding errors during deployment and require large amounts of expert data to achieve reliable performance. To move beyond the limitations of demonstration data, in this work, we propose DexPIE, a post-training framework for dexterous policy improvement from experience collected through real-world deployment. First, DexPIE enables effective exploration coverage through a dexterous-hand-adapted intervention system and multi-stage DAgger-style data collection across initial a...
| Authors | Ruizhe Liao, Wenrui Chen, Liangji Zeng et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 June 2026 |
| Last updated at source | 6 October 2026 |
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