Learning Visuomotor Policy for Multi-Robot Laser Tag Game

Abstract

In this paper, we study multi-robot laser tag, a simplified yet practical shooting-game-style task. Classic modular approaches on these tasks face challenges such as limited observability and reliance on depth mapping and interrobot communication. To overcome these issues, we present an end-to-end visuomotor policy that maps images directly to robot actions. We train a high-performing teacher policy with multi-agent reinforcement learning and distill its knowledge into a vision-based student policy. Technical designs, including a permutation-invariant feature extractor and depth–heatmap input, improve performance over standard architectures. Our policy outperforms classic methods by 16.7% in hitting accuracy and 6% in collision avoidance, and is successfully deployed on real robots. Code will be released publicly.