Mao, Xiaofeng; Giudici, Gabriele; Coppola, Claudio; Althoefer, Kaspar; Farkhatdinov, Ildar; Li, Zhibin; Jamone, Lorenzo; (2024) DexSkills: Skill Segmentation Using Haptic Data for Learning Autonomous Long-Horizon Robotic Manipulation Tasks. In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (pp. pp. 5104-5111). IEEE: Abu Dhabi, United Arab Emirates.

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Abstract

Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems. While learning from human demonstrations has shown encouraging results, they require extensive data collection for training. Hence, decomposing long-horizon tasks into reusable primitive skills is a more efficient approach. To achieve so, we developed DexSkills, a novel supervised learning framework that addresses long-horizon dexterous manipulation tasks using primitive skills. DexSkills is trained to recognize and replicate a select set of skills using human demonstration data, which can then segment a demonstrated long-horizon dexterous manipulation task into a sequence of primitive skills to achieve one-shot execution by the robot directly. Significantly, DexSkills operates solely on proprioceptive and tactile data, i.e., haptic data. Our real-world robotic experiments show that DexSkills can accurately segment skills, thereby enabling autonomous robot execution of a diverse range of tasks.

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