  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.
