端到端视觉动作策略让多机器人激光射击更准更安全
Learning Visuomotor Policy for Multi-Robot Laser Tag Game
- 直接从图像映射动作，无需深度图和机器人通信
- 比传统方法命中率高16.7%，碰撞规避能力提升6%
- 适合真实机器人部署，支持复杂对抗场景
本文研究多机器人激光射击游戏这一简化但实用的射击类任务。传统模块化方法存在可观测性差、依赖深度图和机器人间通信等问题。为此，我们提出一种端到端视觉动作策略，直接将图像映射为机器人动作。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。技术设计包括排列不变特征提取器和深度热图输入，显著优于标准架构。所提策略在命中准确率上比经典方法提升16.7%，碰撞规避能力提升6%，并在真实机器人上成功部署。代码将公开发布。
原文摘要 · Abstract (English)
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.
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