Phase-Amplitude Reduction-Based Imitation Learning

• URL: http://arxiv.org/abs/2406.03735v1
• Date: Thu, 6 Jun 2024 04:19:55 GMT
• Title: Phase-Amplitude Reduction-Based Imitation Learning
• Authors: Satoshi Yamamori, Jun Morimoto,
• Abstract summary: We propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Unlike previous dynamical system-based imitation learning approaches, our proposed method allows the robot to imitate a limit cycle trajectory. Our findings confirm that our proposed method can more accurately generate transient movements to converge on a target periodic attractor.
• Score: 0.9668407688201356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Imitating human movement trajectories is recognized as a promising strategy for generating a range of human-like robot movements. Unlike previous dynamical system-based imitation learning approaches, our proposed method allows the robot not only to imitate a limit cycle trajectory but also to replicate the transient movement from the initial or disturbed state to the limit cycle. Consequently, our method offers a safer imitation learning approach that avoids generating unpredictable motions immediately after disturbances or from a specified initial state. We first validated our proposed method by reconstructing a simple limit-cycle attractor. We then compared the proposed approach with a conventional method on a lemniscate trajectory tracking task with a simulated robot arm. Our findings confirm that our proposed method can more accurately generate transient movements to converge on a target periodic attractor compared to the previous standard approach. Subsequently, we applied our method to a real robot arm to imitate periodic human movements.

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