Jaquier, Noémie Rozo, Leonel Caldwell, Darwin G Calinon, Sylvain 2020 <jats:p> Body posture influences human and robot performance in manipulation tasks, as appropriate poses facilitate motion or the exertion of force 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 applying a specific force. In this context, this article 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. </jats:p> application/pdf https://sonar.ch/global/documents/32411 eng info:eu-repo/semantics/altIdentifier/doi/10.1177/0278364920946815 info:eu-repo/semantics/altIdentifier/issn/0278-3649 info:eu-repo/semantics/openAccess The International Journal of Robotics Research. - SAGE Publications. - 2020, p. 027836492094681 Mechanical Engineering Modelling and Simulation Electrical and Electronic Engineering Software Applied Mathematics Artificial Intelligence Geometry-aware manipulability learning, tracking, and transfer http://purl.org/coar/resource_type/c_6501