Improving biped walk stability with complementary corrective demonstration

We contribute a method for improving the skill execution performance of a robot by complementing an existing algorithmic solution with corrective human demonstration. We apply the proposed method to the biped walking problem, which is a good example of a complex low level skill due to the complicated dynamics of the walk process in a high dimensional state and action space. We introduce an incremental learning approach to improve the Nao humanoid robot’s stability during walking. First, we identify, extract, and record a complete walk cycle from the motion of the robot as it executes a given walk algorithm as a black box. Second, we apply offline advice operators for improving the stability of the learned open-loop walk cycle. Finally, we present an algorithm to directly modify the recorded walk cycle using real time corrective human demonstration. The demonstrator delivers the corrective feedback using a commercially available wireless game controller without touching the robot. Through the proposed algorithm, the robot learns a closed-loop correction policy for the open-loop walk by mapping the corrective demonstrations to the sensory readings received while walking. Experiment results demonstrate a significant improvement in the walk stability.