Morphological control as guiding principle in physiology and medical applications

Keynote lecture
Today, the concept of morphological computation, i.e. the idea that the control of a system is at least partially embodied in its functional physical or chemical dynamics, has become a well - established approach in robotics. Novel designs that exhibit robust behavior and adaptability result from a blurring of the distinction between hard- and software. Such designs are often inspired by biology: Not only by the means (i.e. materials) of nature but also by the ways (organizational principles) nature employs. Remarkably, morphological control connects technology and biology in a bi – directional manner. We apply the concepts of morphological control in order to support, complement and substitute physiological processes in medical problem settings. Thereby, we exploit the ideas of morphological control in systems on different length scales and governed by classical as well as statistical mechanics. In a first group of investigations, we aim at the implementation of an inflatable support system for patients with movement impairments. Quite often, the loss of the ability to control one’s movements is interpreted as a decrease of the computational power of neural system. Based on experimental evidence, we hypothesize that at least in some cases neural control is still at its full power. But due to changes of the mechanical properties of the aging body, the control problem has become more difficult, i.e. the body experiences a decrease of its morphological control power. By the use of inflatable exoskeletal devices, we aspire to influence the mechanical properties of the body in such a manner that morphological control is re – established. The inflatable structures we employ are based on the “tensairity” principle. Recent research demonstrated the feasibility of actuated soft structures based on tenairity. In that sense, soft robotics meets morphological control in the medical field. On the microscopic level, we focus on cell – dynamics. We assume the cell as a system tha