Bayesian uncertainty modeling for programming by demonstration

Abstract

Programming by Demonstration allows to transfer skills from human demonstrators to robotic systems by observation and reproduction. One aspect that is often overlooked is that humans show different trajectories over multiple demonstrations for the same task. Observed movements may be more precise in some phases and more diverse in others. It is well-known that the variability of the execution carries important information about the task. Therefore, we propose a Bayesian approach to model uncertainties from training data and to infer them in regions with sparse information. The approach is validated in simulation, where it shows higher precision than existing methods, and a robotic experiment with variance based impedance adaptation.