Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

Authors

Susanne Still, Bernhard Schölkopf, Klaus Hepp, Rodney Douglas

Abstract

To control the walking gaits of a four-legged robot we present a novel neuromorphic VLSI chip that coordinates the relative phasing of the robot's legs similar to how spinal Central Pattern Generators are believed to control vertebrate locomotion [3]. The chip controls the leg move(cid:173) ments by driving motors with time varying voltages which are the out(cid:173) puts of a small network of coupled oscillators. The characteristics of the chip's output voltages depend on a set of input parameters. The rela(cid:173) tionship between input parameters and output voltages can be computed analytically for an idealized system. In practice, however, this ideal re(cid:173) lationship is only approximately true due to transistor mismatch and off(cid:173) sets. Fine tuning of the chip's input parameters is done automatically by the robotic system, using an unsupervised Support Vector (SV) learning algorithm introduced recently [7]. The learning requires only that the description of the desired output is given. The machine learns from (un(cid:173) labeled) examples how to set the parameters to the chip in order to obtain a desired motor behavior.