Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies
This work addresses the problem of enabling robots to adapt to inconsistent human rewards in human-robot interaction, though it is incremental as it applies an existing algorithm to new scenarios.
The paper tackled the challenge of robots learning from variable human feedback by combining model-based and model-free reinforcement learning strategies in simulated human-robot interaction tasks, showing that the coordination algorithm achieved maximal performance with minimal computational cost and robustly handled feedback variability.
An important current challenge in Human-Robot Interaction (HRI) is to enable robots to learn on-the-fly from human feedback. However, humans show a great variability in the way they reward robots. We propose to address this issue by enabling 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 right cubes in the right boxes, and a more complicated version of this task where cubes have to be placed in a specific order. We show that our existing MB-MF coordination algorithm previously tested in robot navigation works well here without retuning parameters. It leads to the maximal performance while producing the same minimal computational cost as MF alone. Moreover, the algorithm gives a robust performance no matter the variability of the simulated human feedback, while each strategy alone is impacted by this variability. Overall, the results suggest a promising way to promote robot learning flexibility when facing variable human feedback.