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.