2022/08/01 by Simon Stepputtis, Stepputtis, Simon, Maryam Bandari +5 · 7 citations
Computer Science · Engineering · Medicine · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Muscle activation and electromyography studies #Robot Manipulation and Learning #Robotics (cs.RO) #Stroke Rehabilitation and Recovery #cs.HC #cs.RO
paper · pdf · doi:10.48550/arxiv.2208.00596
Accepted to the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan
arxiv created 2022/08/01 · openalex publication_date 2022/08/01 · arxiv updated 2022/08/02 · openalex created_date 2022/08/03 · openalex updated_date 2026/07/28
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/