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학술대회 C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning
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저자
김태우, 이주행
발행일
202006
출처
International Conference on Robotics and Automation (ICRA) 2020, pp.8425-8432
DOI
https://dx.doi.org/10.1109/ICRA40945.2020.9196948
협약과제
19HS6200, 고령 사회에 대응하기 위한 실환경 휴먼케어 로봇 기술 개발, 이재연
초록
Motion retargeting between heterogeneous polymorphs with different sizes and kinematic configurations requires a comprehensive knowledge of (inverse) kinematics. Moreover, it is non-trivial to provide a kinematic independent general solution. In this study, we developed a cyclic three-phase optimization method based on deep reinforcement learning for human-robot motion retargeting. The motion retargeting learning is performed using refined data in a latent space by the cyclic and filtering paths of our method. In addition, the human- in-the-loop based three-phase approach provides a framework for the improvement of the motion retargeting policy by both quantitative and qualitative manners. Using the proposed C- 3PO method, we were successfully able to learn the motion retargeting skill between the human skeleton and motion of the multiple robots such as NAO, Pepper, Baxter and C-3PO.
KSP 제안 키워드
Deep reinforcement learning, Different sizes, Human Skeleton, Human-Robot, Latent space, Motion Retargeting, PO method, Phase Optimization, Reinforcement Learning(RL), Robot motion, Three-Phase