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 |
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| Added by: | [UNK] |
| Total mark: | 0 |
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