Learning Adaptive Dexterous Grasping from Single Demonstrations

• URL: http://arxiv.org/abs/2503.20208v1
• Date: Wed, 26 Mar 2025 04:05:50 GMT
• Title: Learning Adaptive Dexterous Grasping from Single Demonstrations
• Authors: Liangzhi Shi, Yulin Liu, Lingqi Zeng, Bo Ai, Zhengdong Hong, Hao Su,
• Abstract summary: This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection.<n>AdaDexGrasp learns a library of grasping skills from a single human demonstration per skill and selects the most suitable one using a vision-language model (VLM)<n>We evaluate AdaDexGrasp in both simulation and real-world settings, showing that our approach significantly improves RL efficiency and enables learning human-like grasp strategies across varied object configurations.
• Score: 27.806856958659054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How can robots learn dexterous grasping skills efficiently and apply them adaptively based on user instructions? This work tackles two key challenges: efficient skill acquisition from limited human demonstrations and context-driven skill selection. We introduce AdaDexGrasp, a framework that learns a library of grasping skills from a single human demonstration per skill and selects the most suitable one using a vision-language model (VLM). To improve sample efficiency, we propose a trajectory following reward that guides reinforcement learning (RL) toward states close to a human demonstration while allowing flexibility in exploration. To learn beyond the single demonstration, we employ curriculum learning, progressively increasing object pose variations to enhance robustness. At deployment, a VLM retrieves the appropriate skill based on user instructions, bridging low-level learned skills with high-level intent. We evaluate AdaDexGrasp in both simulation and real-world settings, showing that our approach significantly improves RL efficiency and enables learning human-like grasp strategies across varied object configurations. Finally, we demonstrate zero-shot transfer of our learned policies to a real-world PSYONIC Ability Hand, with a 90% success rate across objects, significantly outperforming the baseline.

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