  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.
