Manipulability Learning, Tracking and Transfer

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, we are propose to analyze the human arms manipulability patterns in industrial activities and we presents a novel manipulability transfer framework, a method that allows robots to learn and reproduce manipulability ellipsoids from humans/experts demonstrations. The proposed approach is built on Riemannian manifold theory and takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices.