Collaboration is one of the most important factors in multi-robot systems. Considering certain real-world applications and to further promote its development, we propose a new benchmark to evaluate multi-robot collaboration in Target Trapping Environment (T2E). In T2E, two kinds of robots (called captor robot and target robot) share the same space. The captors aim to catch the target collaboratively, while the target will try to escape from the trap. Both the trapping and escaping process can use the environment layout to help achieve the corresponding objective, which requires high collaboration between robots and the utilization of the environment. For the benchmark, we present and evaluate multiple learning-based baselines in T2E, and provide insights into regimes of multi-robot collaboration. We also make our benchmark publicly available and encourage researchers from related robotics disciplines to propose, evaluate, and compare their solutions in this benchmark. Our project is released at https://github.com/Dr-Xiaogaren/T2E.

翻译：协作是多机器人系统中最重要的因素之一。为考虑实际应用场景并进一步推动其发展，我们提出一个新的基准测试——目标围捕环境（T2E），用于评估多机器人协作。在T2E中，两种机器人（称为捕手机器人和目标机器人）共享同一空间。捕手机器人旨在通过协作捕捉目标，而目标机器人则会试图逃离围捕。围捕与逃脱过程均可利用环境布局协助实现各自目标，这要求机器人之间高度协作并充分利用环境。针对该基准，我们在T2E中提出并评估了多种基于学习的基线方法，并深入分析了多机器人协作机制。我们还公开了该基准测试，并鼓励相关机器人领域的研究人员在此基准中提出、评估及比较其解决方案。项目代码已在https://github.com/Dr-Xiaogaren/T2E 发布。