Phase-Amplitude Reduction-Based Imitation Learning

2024-06-06

Adv. Robotics

AI Summary

Conventional imitation learning methods focus solely on replicating steady-state periodic trajectories, neglecting transient dynamics and thereby compromising safety during initialization or external disturbances. Method: This paper proposes a novel imitation learning framework based on phase–amplitude dimensionality reduction—the first to integrate phase–amplitude theory into imitation learning. It jointly models limit-cycle dynamics and transient behavior, enabling controllable and predictable convergence from arbitrary initial or perturbed states to the limit cycle. The framework comprises a phase–amplitude dynamical model, a limit-cycle attractor, and a robot kinematic mapping. Results: In simulation, the method significantly improves transient convergence accuracy for lemniscate trajectories. It successfully transfers to a real robotic arm, achieving high-fidelity, robust imitation of human periodic motions. By transcending the steady-state fitting limitation of prior approaches, this work establishes a new paradigm for safety-critical robotic imitation learning.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops imitation learning for human-like robot movements.
• Enables robots to replicate transient and limit cycle movements.
• Improves safety by avoiding unpredictable motions after disturbances.

Innovation

• Phase-amplitude reduction for imitation learning
• Replicates transient and limit cycle movements
• Safer, accurate movement generation post-disturbance