Self-sustaining rhythmic arm motions using neural oscillators

Humans or animals exhibit natural adaptive motions against unexpected disturbances or environment changes. In this paper, we focus on periodic, rhythmic arm motions that can be achieved by using a controller based on neural oscillators. The challenge of this work is to determine appropriate parameters of neural oscillators coupled to a robot arm, accomplishing a given task as well as self-sustaining natural rhythms. For this, an enhanced simulated annealing (SA) algorithm is developed. This work also demonstrates how to technically implement the proposed control scheme to a real robot. Exploiting the entrainment property of neural oscillators coupled to the joints of the arm, we verify that the arm traces a trajectory in such a way that the total energy consumption is minimized, responding to external disturbances.