Susanne Still, Bernhard Schölkopf, Klaus Hepp, Rodney Douglas
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 . 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 . 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.