  Manipulability ellipsoids efficiently capture the human pose and reveal
information about the task at hand. Their use in task-dependent robot teaching
- particularly their transfer from a teacher to a learner - can advance
emulation of human-like motion. Although in recent literature focus is shifted
towards manipulability transfer between two robots, the adaptation to the
capabilities of the other kinematic system is to date not addressed and
research in transfer from human to robot is still in its infancy. This work
presents a novel manipulability domain adaptation method for the transfer of
manipulability information to the domain of another kinematic system. As
manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they
can be viewed as points on the Riemannian manifold of SPD matrices. We are the
first to address the problem of manipulability transfer from the perspective of
point cloud registration. We propose a manifold-aware Iterative Closest Point
algorithm (ICP) with parallel transport initialization. Furthermore, we
introduce a correspondence matching heuristic for manipulability ellipsoids
based on inherent geometric features. We confirm our method in simulation
experiments with 2-DoF manipulators as well as 7-DoF models representing the
human-arm kinematics.
