This award funds initial research on integration of methods for reflexive gait synthesis and adaptation to difficult terrains, for a biped walking robot. The research will explore neural- network-based mechanisms for training a central pattern generator to generate a new gait that is learned by a terrain-monitoring learning and adaptation unit. It will also study the convergence properties of backpropagation and reinforcement learning mechanisms for use in the learning and adaptation unit. The long-term goal of this research in biped robotics is to achieve more agility and mobility than is possible in multilegged walking machines or other forms of machine locomotion, and ultimately to provide capability for mobile robots to augment or replace humans in hazardous operations or in rough terrain.
