  Modeling of physical human-robot collaborations is generally a challenging
problem due to the unpredictive nature of human behavior. To address this
issue, we present a data-efficient reinforcement learning framework which
enables a robot to learn how to collaborate with a human partner. The robot
learns the task from its own sensorimotor experiences in an unsupervised
manner. The uncertainty of the human actions is modeled using Gaussian
processes (GP) to implement action-value functions. Optimal action selection
given the uncertain GP model is ensured by Bayesian optimization. We apply the
framework to a scenario in which a human and a PR2 robot jointly control the
ball position on a plank based on vision and force/torque data. Our
experimental results show the suitability of the proposed method in terms of
fast and data-efficient model learning, optimal action selection under
uncertainties and equal role sharing between the partners.
