Poincaré-Map-Based Reinforcement Learning For Biped Walking

We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately modulate an observed walking pattern. Viapoints are detected from the observed walking trajectories using the minimum jerk criterion. The learning algorithm modulates the via-points as control actions to improve walking trajectories. This decision is based on a learned model of the Poincaré map of the periodic walking pattern. The model maps from a state in the single support phase and the control actions to a state in the next single support phase. We applied this approach to both a simulated robot model and an actual biped robot. We show that successful walking policies are acquired.