  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.
