Reinforcement learning for a biped robot to climb sloping surfaces

A neural network mechanism is proposed to modify the gait of a biped robot that walks on sloping surfaces using sensory inputs. The robot climbs a sloping surface from a level surface with no priori knowledge of the inclination of the surface. By training the neural network while the robot is walking, the robot adjusts its gait and finally forms a gait that is as stable as when it walks on the level surface. The neural network is trained by a reinforcement learning mechanism while proportional and integral (PI) control is used for position control of the robot joints. Experiments of static and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism. (C) 1997 John Wiley & Sons, Inc.