Motion Strategy Using Opponent Player's Serial Learning for Air-Hockey Robots

In recent years, there have been many studies on sports robots that can play against humans, including studies on the strategies that sports robots use by taking into account the physical conditions of their opponents. However, there have been few studies on strategies that take into account psychological conditions of the opponents, such as carelessness and habituation. This paper proposes a motion strategy for an air-hockey robot that intentionally coaxes the opponent to learn the robot's attack sequence and then uses a different attack sequence to catch the opponent off guard. We explicitly model the change in reaction time during serial learning and represent the opponent's reaction as an evaluation function. By applying this evaluation function to a game tree and selecting the optimal motion, the robot could catch the opponent by surprise. Actual experiments with several subjects confirmed the effectiveness of the proposed method.