2019/09/30 by Ayumu Sasagawa, Kazuki Fujimoto, Sho Sakaino +1 · 56 citations
Computer Science · Engineering · Neuroscience · Psychology · #Action (physics) #Action Observation and Synchronization #Control (management) #Generalization #Imitation #Motor Control and Adaptation #Object (grammar) #Robot #Robot Manipulation and Learning #Task (project management) #cs.RO
paper · pdf · doi:10.1109/lra.2020.3011353
published in IEEE Robotics and Automation Letters 5(4), 6169-6176 (Institute of Electrical and Electronics Engineers) · Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
openalex created_date 2020/07/23 · openalex publication_date 2020/07/23 · arxiv created 2021/01/20 · arxiv updated 2021/01/21 · openalex updated_date 2026/08/05
Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.