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 inter robot 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.

翻译：本文研究多机器人激光对抗这一简化而实用的射击类游戏任务。经典模块化方法在此类任务中面临观测能力有限、依赖深度建图及机器人间通信等挑战。为克服这些问题，我们提出一种端到端的视觉运动策略，可直接将图像映射为机器人动作。我们通过多智能体强化学习训练高性能教师策略，并将其知识蒸馏至基于视觉的学生策略中。包括置换不变特征提取器和深度热图输入在内的技术设计，使本方法在性能上超越了标准架构。我们的策略在命中准确率上优于经典方法16.7%，在碰撞规避上提升6%，并已成功部署于真实机器人平台。代码将公开发布。