Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2021
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| _version_ | 1866910667757322240 |
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| author | Balakuntala, Mythra V. Kaur, Upinder Ma, Xin Wachs, Juan Voyles, Richard M. |
| author_facet | Balakuntala, Mythra V. Kaur, Upinder Ma, Xin Wachs, Juan Voyles, Richard M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_01296 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering Balakuntala, Mythra V. Kaur, Upinder Ma, Xin Wachs, Juan Voyles, Richard M. Robotics 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. |
| title | Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering |
| topic | Robotics |
| url | https://arxiv.org/abs/2103.01296 |