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Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment

arXiv 2308.08862 ↗First posted 17 Aug 2023cs.RO cs.MAcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2023.

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.