Full body motion adaption based on task-space distance meshes

This paper presents a novel robot pose measure for human movement imitation based entirely on the Euclidean distance information between any two links of a robot and any link and object in the robot's environment in a Cartesian task space. A Hidden Markov Model is used to encode the spatio-temporal information of multiple demonstrations. In combination with Gaussian Mixture Regression for extracting the important task properties, feasible full-body motion adaption can be achieved. The method is suited for use with a humanoid robot by considering additional constraints like balance control and collision avoidance. In order to tackle modeling errors occurring due to the human movement demonstration and the robotic reproduction, a manipulability based weighting scheme is proposed. Complexity reduction of the otherwise redundant pose measure is performed based upon a mechanical analogy of an interconnected spring system. Experiments are conducted using a HRP-4 robot and display the applicability of the presented methods for robotic full-body motion imitation tasks.