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arXiv · 2103.01296

Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering

Mythra V. Balakuntala·Upinder Kaur·Xin Ma·Juan Wachs·Richard M. Voyles
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Abstract

Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills -- cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100\% for the peeling and writing skill, and 80\% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.

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Mythra V. Balakuntala, Upinder Kaur, Xin Ma, Juan Wachs, Richard M. Voyles. 2021-03-01. Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering. https://arxiv.org/abs/2103.01296

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