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An important current challenge in Human-Robot Interaction (HRI) is to enablerobots to learn on-the-fly from human feedback. However, humans show a greatvariability in the way they reward robots. We propose to address this issue byenabling the robot to combine different learning strategies, namely model-based(MB) and model-free (MF) reinforcement learning. We simulate two HRI scenarios:a simple task where the human congratulates the robot for putting the rightcubes in the right boxes, and a more complicated version of this task wherecubes have to be placed in a specific order. We show that our existing MB-MFcoordination algorithm previously tested in robot navigation works well herewithout retuning parameters. It leads to the maximal performance whileproducing the same minimal computational cost as MF alone. Moreover, thealgorithm gives a robust performance no matter the variability of the simulatedhuman feedback, while each strategy alone is impacted by this variability.Overall, the results suggest a promising way to promote robot learningflexibility when facing variable human feedback.

Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies