Motion Control of Bionic Robots via Biomimetic Learning

Locomotion of animals is highly coordinating, efficient and full of maneuverability. Such outstanding and unique characteristics are acquired through millions of years of evolution. It is highly desirable to enhance robots with such characteristics, which is one of the ultimate objectives of biomimetic research. To achieve this goal, in this paper, a biomimetic learning approach for locomotion generation of bionic robots is proposed. The main feature of the learning approach is adopting multiple general internal models (GIMs) to learn and regenerate coordinated animal behaviors. This work discusses the basic mechanism of the proposed GIM-based learning approach. Moreover, two cases with different bionic robots are studied to explore the effectiveness and the generality of the proposed approach. This paper summarizes the recent development of biomimetic learning. It is noted that this work is devoted to the contribution of Late Professor Jian-xin Xu in this area.