Gait synthesis for a biped robot climbing sloping surfaces using neural networks. II. Dynamic learning

For pt.I see ibid., p.2601-6 (1992). A neural network mechanism is proposed to modify the rhythmic motion (gait) of a two-legged robot when walking on sloping surfaces using a sensory input. The robot starts walking on a terrain with no previous knowledge, but accumulates walking experience during walking, thus, constantly improving its walking gait. The proposed network consists of 20 reciprocally inhibited and excited neurons. An unsupervised learning rule was implemented using reinforcement signals. A dynamic learning approach is proposed where the network learns constantly during the walking process. The training is conducted while the robot is in motion. The algorithm was verified by simulation