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- W4313121131 abstract "In this paper, we discuss a framework for teaching bimanual manipulation tasks by imitation. To this end, we present a system and algorithms for learning compliant and contact-rich robot behavior from human demonstrations. The presented system combines insights from admittance control and machine learning to extract control policies that can (a) recover from and adapt to a variety of disturbances in time and space, while also (b) effectively leveraging physical contact with the environment. We demonstrate the effectiveness of our approach using a real-world insertion task involving multiple simultaneous contacts between a manipulated object and insertion pegs. We also investigate efficient means of collecting training data for such bimanual settings. To this end, we conduct a human-subject study and analyze the effort and mental demand as reported by the users. Our experiments show that, while harder to provide, the additional force/torque information available in teleoperated demonstrations is crucial for phase estimation and task success. Ultimately, force/torque data substantially improves manipulation robustness, resulting in a 90% success rate in a multipoint insertion task. Code and videos can be found at https://bimanualmanipulation.com/" @default.
- W4313121131 created "2023-01-06" @default.
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- W4313121131 date "2022-10-23" @default.
- W4313121131 modified "2023-10-16" @default.
- W4313121131 title "A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies" @default.
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- W4313121131 doi "https://doi.org/10.1109/iros47612.2022.9981802" @default.
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