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In this paper we present an inverse optimal control based transfer of motions from human experiments to humanoid robots and apply it to walking in constrained environments. To this end we introduce a 3D template model, which describes motion on the basis of center of mass trajectory, foot trajectories, upper body orientation and phase duration. Despite of its abstract architecture with prismatic joints combined with damped series elastic actuators instead of knees, the model (including dynamics and constraints) is suitable to describe both, human and humanoid locomotion with appropriate parameters. We present and apply an inverse optimal control approach to identify optimality criteria based on human motion capture experiments. The identified optimal strategy is then transferred to the humanoid robot for gait generation by solving an optimal control problem, which takes into account the properties of the robot and differences in the environment. The results of this approach are the center of mass trajectory, the foot trajectories, the torso orientation, and the single and double support phase durations for a sequence of multiple steps allowing the humanoid robot to walk within a new environment. We present one computational cycle (from motion capture data to an optimized robot motion) for the example of walking over irregular step stones with the aim to transfer the motion to two very different humanoid robots (iCub Heidelberg01 and HRP-2 14). The transfer of these optimized robot motions to the real robots by means of inverse kinematics is work in progress and not part of this paper.

An Inverse Optimal Control Approach for the Transfer of Human Walking Motions in Constrained Environment to Humanoid Robots