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

August 17, 2023 ยท Entered Twilight ยท ๐Ÿ› IEEE transactions on industrial electronics (1982. Print)

๐Ÿ’ค TWILIGHT: Eternal Rest
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Repo contents: README.md, envs, offpolicy, onpolicy, requirements.txt, scene, setup.py

Authors Hao Zhang, Jiaming Chen, Jiyu Cheng, Yibin Li, Simon X. Yang, Wei Zhang arXiv ID 2308.08862 Category cs.RO: Robotics Cross-listed cs.MA Citations 10 Venue IEEE transactions on industrial electronics (1982. Print) Repository https://github.com/Dr-Xiaogaren/T2E โญ 13 Last Checked 2 months ago
Abstract
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.
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