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.

翻译：利用复杂身体动力学一直是机器人学中的长期挑战。软体动力学是与环境交互中高度复杂性的典型实例。越来越多的研究报告指出，这些动力学可用作计算资源。这包括McKibben气动人工肌肉，它是一种典型的软体致动器。本研究表明，通过物理储层计算框架，可将包括周期和混沌动力学在内的各种动力学以及完整的分岔结构嵌入到气动人工肌肉中。这些结果提示，利用这种分岔嵌入能力，可以嵌入训练数据中未呈现的动力学。这意味着，通过仅学习特定模式，即可将各种定性不同的模式嵌入气动人工肌肉中，而无需设计并学习所有目标所需的模式。因此，本研究为简化机器人设备及控制训练开辟了新途径，通过减少外部模式生成器以及控制所需的训练数据量和类型来实现。