%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Jaquier_IJRR_2020,
         author = {Jaquier, N. and Rozo, L. and Caldwell, D. G. and Calinon, Sylvain},
       keywords = {differential kinematics, manipulability ellipsoids, programming by demonstration, Riemannian manifolds, robot learning},
       projects = {TACT-HAND},
          title = {Geometry-aware Manipulability Learning, Tracking and Transfer},
        journal = {International Journal of Robotic Research},
         volume = {40},
         number = {2-3},
           year = {2021},
          pages = {624-650},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2021/Jaquier_IJRR_2020.pdf}
}