Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Jun Morimoto, Christopher Atkeson
We developed a robust control policy design method in high-dimensional state space by using differential dynamic programming with a minimax criterion. As an example, we applied our method to a simulated ﬁve link biped robot. The results show lower joint torques from the optimal con- trol policy compared to a hand-tuned PD servo controller. Results also show that the simulated biped robot can successfully walk with unknown disturbances that cause controllers generated by standard differential dy- namic programming and the hand-tuned PD servo to fail. Learning to compensate for modeling error and previously unknown disturbances in conjunction with robust control design is also demonstrated.