Jaquier, N., Rozo, L., Caldwell, D.G. and Calinon, S. (2021)
Geometry-aware Manipulability Learning, Tracking and Transfer
International Journal of Robotics Research (IJRR), 40:2-3, 624-650.


Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel manipulability transfer framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.

Bibtex reference

	author="Jaquier, N. and Rozo, L. and Caldwell, D. G. and Calinon, S.",
	title="Geometry-aware Manipulability Learning, Tracking and Transfer",
	journal="International Journal of Robotics Research ({IJRR})",
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