Phase-Amplitude Reduction-Based Imitation Learning

TL;DR

This paper introduces a phase-amplitude reduction-based imitation learning framework that enables robots to imitate both steady and transient human movements safely and accurately, improving upon previous methods.

Contribution

The study presents a novel imitation learning approach using phase-amplitude reduction that captures transient dynamics and enhances safety and accuracy in robot movement imitation.

Findings

More accurate transient movement generation compared to standard methods.

Successful application to real robot arm imitating human movements.

Validated on simple and complex trajectories with improved convergence.

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.