Modeling and Predicting Human Actions in Soccer Using Tensor-SOM

As robots become increasingly integrated into society, a future in which humans and robots collaborate is expected. In such a cooperative society, robots must possess the ability to predict human behavior. This study investigates a human–robot cooperation system using RoboCup soccer as a testbed, where a robot observes human actions, infers their intentions, and determines its own actions accordingly. Such problems have typically been addressed within the framework of multi-agent systems, where the entity performing an action is referred to as an ‘agent’, and multiple agents cooperate to complete a task. However, a system capable of performing cooperative actions in an environment where both humans and robots coexist has yet to be fully developed. This study proposes an action decision system based on self-organizing maps (SOM), a widely used unsupervised learning model, and evaluates its effectiveness in promoting cooperative play within human teams. Specifically, we analyze futsal game data, where the agents are professional futsal players, as a test case for the multi-agent system. To this end, we employ Tensor-SOM, an extension of SOM that can handle multi-relational datasets. The system learns from this data to determine the optimal movement speeds in x and y directions for each agent’s position. The results demonstrate that the proposed system successfully determines optimal movement speeds, suggesting its potential for integrating robots into human team coordination.