Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning

TL;DR

This paper introduces an autoregressive learning method for bilateral control-based imitation learning in robotics, enhancing long-term motion generation and generalization over conventional approaches.

Contribution

It proposes a novel neural network model enabling autoregressive learning in bilateral control-based imitation learning, improving long-term motion generation and success rates.

Findings

Performance surpasses conventional methods.

Achieves the highest success rate in experiments.

Generates desirable motion with high environmental adaptability.

Abstract

Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has been studied as an efficient and high-performance method, and imitation learning based on bilateral control has been proposed as a method that can realize fast motion. However, because this method cannot implement autoregressive learning, this method may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning. A new neural network model for implementing autoregressive learning is proposed. In this study, three types of experiments are conducted to verify the effectiveness of the proposed method. The performance is improved compared to conventional approaches; the proposed method has the highest rate of success. Owing to the structure and autoregressive learning of the proposed model, the proposed method can generate the desirable motion for successful tasks and have a high generalization ability for environmental changes.