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Manipulability ellipsoids efficiently capture the human pose and revealinformation about the task at hand. Their use in task-dependent robot teaching- particularly their transfer from a teacher to a learner - can advanceemulation of human-like motion. Although in recent literature focus is shiftedtowards manipulability transfer between two robots, the adaptation to thecapabilities of the other kinematic system is to date not addressed andresearch in transfer from human to robot is still in its infancy. This workpresents a novel manipulability domain adaptation method for the transfer ofmanipulability information to the domain of another kinematic system. Asmanipulability matrices/ellipsoids are symmetric positive-definite (SPD) theycan be viewed as points on the Riemannian manifold of SPD matrices. We are thefirst to address the problem of manipulability transfer from the perspective ofpoint cloud registration. We propose a manifold-aware Iterative Closest Pointalgorithm (ICP) with parallel transport initialization. Furthermore, weintroduce a correspondence matching heuristic for manipulability ellipsoidsbased on inherent geometric features. We confirm our method in simulationexperiments with 2-DoF manipulators as well as 7-DoF models representing thehuman-arm kinematics.

Human-to-Robot Manipulability Domain Adaptation with Parallel Transport and Manifold-Aware ICP