Shaping Social Robot to Play Games with Human Demonstrations and Evaluative Feedback

In this paper, building on recent advances in the fields of gaming AI and social robotics, we present a new approach to facilitate the social robot Haru to imitate game strategies from human players’ demonstrated trajectories and evaluative feedback in a real-time two-player game. Our research shows that Haru is able to learn and imitate human different game strategies from human players in a human time scale. In addition, our results show that human evaluative feedback plays an important role in allowing Haru to obtain a better performance via our method than human player’s demonstrations. Finally, results of our user study indicate that Haru imitating human player’s game strategies via our method is perceived to be more human-like and have better game performance and experience than self-learning from pre-defined reward functions via traditional deep reinforcement learning.