Abstract

There has been an increasing interest in the concept of “mechanical intelligence” in the design and control of robots. It has been known, for example, that control of dynamic motions such as walking and running can be significantly simplified and more energetically efficient when the mechanical dynamics are set out for physical self-stabilization. In general, self-organization of behaviors based on physical system-environment interactions is the key to understand the autonomy of intelligent adaptive systems. In this lecture, I introduce our efforts on developing and understanding self-organizing machines in the real world, which are structured into three basic research components. The first research component aims to construct the principles of self-organization through mechanical system-environment interactions such as springy legs interacting with the ground for locomotion purposes. These case studies provide us fundamentals of emergent behaviors because system-environment interac tions are the basis of self-organization in the real-world. Second, we have been also exploring self-organization of motion control processes, in which we study the underlying mechanisms of sensory-motor calibration of complex musculoskeletal dynamical systems. Together with our collaborating neurophysiologists, we investigate how muscle twitches during sleep could provide basic circuitry for sensory-motor coordination of spinal reflexes. And third, we also investigate the use of rapid prototyping technologies to autonomously design, assemble, and test physical robots for model-free optimization of robot designs. Through these case studies of dynamic system-environment interactions, we discuss implications of embodied intelligence and its research directions in the future.