Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning
This work addresses the challenge of enabling robots to execute tasks automatically with better generalization for environmental changes, though it appears incremental as it builds on existing imitation learning methods.
The paper tackles the problem of generating desirable long-term robot motions by proposing autoregressive learning for bilateral control-based imitation learning, resulting in improved performance with the highest success rate compared to conventional approaches.
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.