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Harnessing complex body dynamics has been a long-standing challenge inrobotics. Soft body dynamics is a typical example of high complexity ininteracting with the environment. An increasing number of studies have reportedthat these dynamics can be used as a computational resource. This includes theMcKibben pneumatic artificial muscle, which is a typical soft actuator. Thisstudy demonstrated that various dynamics, including periodic and chaoticdynamics, could be embedded into the pneumatic artificial muscle, with theentire bifurcation structure using the framework of physical reservoircomputing. These results suggest that dynamics that are not presented intraining data could be embedded by using this capability of bifurcationembeddment. This implies that it is possible to embed various qualitativelydifferent patterns into pneumatic artificial muscle by learning specificpatterns, without the need to design and learn all patterns required for thepurpose. Thus, this study sheds new light on a novel pathway to simplify therobotic devices and training of the control by reducing the external patterngenerators and the amount and types of training data for the control.

Embedding bifurcations into pneumatic artificial muscle