Active stabilization of a humanoid robot for real-time imitation of a human operator

Abstract

Imitating the motion of a human operator is an intuitive and efficient way to make humanoid robots perform complex, human-like behaviors. With the help of recently introduced affordable and real-time depth sensors, the real time imitation of human behavior has become more feasible. However, due to their small footprint and high center of mass, humanoid robots are not inherently stable. The momentum generated by dynamic upper body movements can induce instabilities that are often large enough to make the robot fall down. In this work, we describe a motion controller for a humanoid robot where the upper body is controlled in real time to imitate a human teacher, and the lower body is reactively stabilized based on the current measured state of the robot. Instead of relying on the accuracy of robot dynamics, we use biomechanically motivated push recovery controllers to stabilize the robot against unknown perturbations that include possible impacts. We demonstrate our approach experimentally on a small humanoid robot platform.