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Human-robot handovers are characterized by high uncertainty and poorstructure of the problem that make them difficult tasks. While machine learningmethods have shown promising results, their application to problems with largestate dimensionality, such as in the case of humanoid robots, is still limited.Additionally, by using these methods and during the interaction with the humanoperator, no guarantees can be obtained on the correct interpretation ofspatial constraints (e.g., from social rules). In this paper, we present PolicyImprovement with Spatio-Temporal Affordance Maps -- $\pi$-STAM, a noveliterative algorithm to learn spatial affordances and generate robot behaviors.Our goal consists in generating a policy that adapts to the unknown actionsemantics by using affordances. In this way, while learning to perform ahuman-robot handover task, we can (1) efficiently generate good policies withfew training episodes, and (2) easily encode action semantics and, ifavailable, enforce prior knowledge in it. We experimentally validate ourapproach both in simulation and on a real NAO robot whose task consists intaking an object from the hands of a human. The obtained results show that ouralgorithm obtains a good policy while reducing the computational load and timeduration of the learning process.

Learning Human-Robot Handovers Through $\pi$-STAM: Policy Improvement With Spatio-Temporal Affordance Maps