Embedding Bifurcations into Pneumatic Artificial Muscle

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control. This is a study that aims to embed bifurcation structures with periodic and chaotic dynamics into pneumatic artificial muscles. The reuslts imply that dynamics not present in training data can be embedded through the bifurcation structure. This is expected to lead to a fundamental improvement in the efficiency of learning for bodily control in robotics.