Geometry-aware Manipulability Learning, Tracking and Transfer

Understand

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 \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations.