Imitation Learning Based Obstacle Avoidance for MSL Soccer Robot in Offensive Scenario

Abstract

Kontes Robot Sepak Bola Indonesia Beroda (KRSBI-B), inspired by the Middle-Size League (MSL) of RoboCup, serves as a platform to push advancements in autonomous soccer robots in Indonesia. A key requirement for these robots is the ability to perceive their environment and make independent decisions involving recognizing field features, planning the navigation, and playing offensive and defensive tactics. Among these, obstacle avoidance during offensive play is critical, as robots should dynamically navigate while targeting the goal. In line with the theme "Toward Robot Soccer League 2050," this study focuses on developing robots capable of human-like performance in dynamic and competitive settings. To achieve this, we utilize Generative Adversarial Imitation Learning (GAIL), a method that enables robots to learn adaptive navigation strategies from expert demonstrations. Equipped with an omnidirectional camera, the robot identifies obstacles, field lines, and goal positions, integrating this sensory data into its decision-making framework. The system was tested in four scenarios: no obstacles, one obstacle, two obstacles, and three obstacles, with randomized obstacle positions. Success rates of 100%, 99.5%, 92.5%, and 82.5% were recorded, demonstrating the system's effectiveness in navigating complex environments and its potential to enhance robotic soccer performance.