Control-aware mapping of human motion data with stepping for humanoid robots

This paper presents a method for mapping captured human motion with stepping to a humanoid model, considering the current state and the controller behavior. The mapping algorithm modifies the joint angle, trunk and center of mass (COM) trajectories so that the motion can be tracked and desired contact states can be achieved. The mapping is performed in two steps. The first step modifies the joint angle and trunk trajectories to adapt to the robot kinematics and actual contact foot positions. The second step uses a predicted center of pressure (COP) to determine if the balance controller can successfully maintain the robot's balance, and if not, modifies the COM trajectory. Unlike most humanoid control work that handles motion synthesis and control separately, our COM trajectory modification is performed based on the behavior of the robot controller. We verify the approach in simulation using a captured Tai-chi motion that involves unstructured contact state changes.