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 [BibTeX] [Marc21]
Geometry-aware Manipulability Learning, Tracking and Transfer
Type of publication: Journal paper
Citation: Jaquier_IJRR_2020
Publication status: Published
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.
Keywords: differential kinematics, manipulability ellipsoids, programming by demonstration, Riemannian manifolds, robot learning
Projects TACT-HAND
Authors Jaquier, N.
Rozo, L.
Caldwell, D. G.
Calinon, Sylvain
Added by: [UNK]
Total mark: 0
  • Jaquier_IJRR_2020.pdf