On using human movement invariants to generate target-driven anthropomorphic locomotion

We present a method for generating anthropomorphic motion by studying `invariants' in human movements and applying them as kinematic tasks. We recorded whole-body motion of 14 participants during a walking and grasping task and performed a detailed analysis in order to synthesize the stereotypy in human motion as rules. We propose an algorithm that produces the key parameters of motion taking into account the knowledge from human movement, and the limitations of the anthropomorph. We generalize our results such that we can create motion parameters for objects which were not in the original protocol. The algorithmic output is applied in a task based prioritized inverse kinematics solver to generate dynamically stable and realistic anthropomorphic motion. We illustrate our results on the humanoid HRP-2 by making it walk to and grasp objects at various positions.