TY - GEN
T1 - A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies
AU - Stepputtis, Simon
AU - Bandari, Maryam
AU - Schaal, Stefan
AU - Amor, Heni Ben
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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/
AB - 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/
UR - http://www.scopus.com/inward/record.url?scp=85146359915&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146359915&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981802
DO - 10.1109/IROS47612.2022.9981802
M3 - Conference contribution
AN - SCOPUS:85146359915
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 11810
EP - 11817
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Y2 - 23 October 2022 through 27 October 2022
ER -