Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots
This addresses the problem of robot adaptability in dynamic soccer environments for robotics and AI researchers, though it appears incremental as it builds on existing temporal logic methods.
The paper tackles the problem of robots struggling to generalize soccer behaviors when game environments change by proposing a temporal logic approach that adapts behaviors and goals based on environmental semantics, enabling operation in unstructured settings like street soccer. It demonstrates effectiveness through three use cases in different scenarios.
Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach that allows robots' behaviors and goals to adapt to the semantics of the environment. In particular, we present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus modifying dynamically the set of rules and goals to apply. The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street. Three different use cases set in different scenarios are presented to demonstrate the effectiveness of the proposed approach.