Learning Visuomotor Policy for Multi-Robot Laser Tag Game
This work addresses challenges in multi-robot coordination for a practical game-like task, offering an incremental improvement over existing modular approaches.
The paper tackles the problem of multi-robot laser tag by developing an end-to-end visuomotor policy that maps images directly to robot actions, resulting in a 16.7% improvement in hitting accuracy and 6% in collision avoidance over classic methods, with successful real-robot deployment.
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.