Probabilistic Inference of Human Capabilities from Passive Observations

Peter Tisnikar, Gerard Canal, Matteo Leonetti

Abstract: Modern robots need to adapt to diverse human partners with whom they collaborate. To this end, learning a representation of human capabilities enables the robot to personalize their behaviour to their collaborators across multiple tasks. We propose CApability Modeling from Observations (CAMO), a model-based estimation algorithm, in which human capabilities that parameterize a given model are inferred from observations of the human behaviour on known collaborative tasks. We apply the method to joint limit learning in order to predict future trajectories of a 7-DOF manipulator arm. Furthermore, we show that CAMO can be used as a sub-task assignment routine in a simulated human–robot collaboration scenario, allowing the robot to adapt its task allocation to perform tasks that the person is not able to do.