Adaptive gait generation via physiological controls

We present a control methodology for locomotion using virtual oscillators whose nonlinear dynamics is generated via a couple of mutually inhibited neurons. A controller that rotates a joint is driven by a neural output, and the state of the neural oscillator is entrained by the signal of the controller in return. Such mutual feedback supplies stable controls of rhythmic motions for variable conditions. Controllers are individually assigned to joints of an articulated figure, and the corresponding oscillators mutually supply the neural signals in order to synchronize periodical movements without using a central supervisor. Our neural pattern generator can control various locomotive patterns in a common mechanism with a small number of parameters, and can flexibly and smoothly control the patterns while maintaining global coordination. Such features are suitable for real-time, interactive, and on-the-fly controls of virtual characters