Embedding bifurcations into pneumatic artificial muscle

Abstract: Harnessing complex body dynamics has been a long-standing challenge in robotics. Soft body dynamics is a typical example of high complexity in interacting with the environment. An increasing number of studies have reported that these dynamics can be used as a computational resource. This includes the McKibben pneumatic artificial muscle, which is a typical soft actuator. This study demonstrated that various dynamics, including periodic and chaotic dynamics, could be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics that are not presented in training data could be embedded by using this capability of bifurcation embeddment. This implies that it is possible to embed various qualitatively different patterns into pneumatic artificial muscle by learning specific patterns, without the need to design and learn all patterns required for the purpose. Thus, this study sheds new light on a novel pathway to simplify the robotic devices and training of the control by reducing the external pattern generators and the amount and types of training data for the control. Main Text: INTRODUCTION Recent studies have revealed that mechanical devices can be designed to use their body dynamics for desired information processing, such as a mechanical random number generator (1) and mechanical neural networks (2). Furthermore, the natural dynamics of mechanical bodies not designed for computation can be used as an information processing resource. The complex dynamics in soft robotic arms, which are inspired by the octopus, can be used for real-time computation, embedding a timer, and controlling the arm by employing the approach of physical reservoir computing (PRC) (3-7).