Phase-dependent trajectory optimization for CPG-based biped walking using path integral reinforcement learning

In this study, we introduce a phase-dependent trajectory optimization method for Central Pattern Generator (CPG)-based biped walking controllers. By exploiting the synchronization property of the CPG controller, many legged locomotion studies have shown that the CPG-based walking controller is robust against external perturbations and works well in real environments. However, due to the nonlinear dynamic property of the coupled oscillator system composed of the CPG controller and the robot, analytically designing the biped trajectory to satisfy the requirements of a target walking pattern is rather difficult. Therefore, using a nonlinear optimization method is reasonable to improve the walking trajectory. To optimize the walking trajectory, a model-free optimal control method is preferable because precise modeling of the ground contact is difficult. On the other hand, model-free trajectory optimization methods have been considered as quite computationally demanding approach. However, because of recent advances in the nonlinear trajectory optimization method, using the model-free optimization method is now a realistic approach for biped trajectory optimization. We use a path integral reinforcement learning method to improve the biped walking trajectory for CPG-based walking controllers.