逃げ場なし：ターゲット捕獲環境におけるマルチロボット協調のベンチマーク
Nowhere to Go: Benchmarking Multi-robot Collaboration in Target Trapping Environment
2種類のロボットが同じ空間で捕獲側と逃走側に分かれ、環境レイアウトを活用しながら協調して目標を達成するマルチロボット協調の新ベンチマークT2Eを提案し、複数の学習ベース手法を評価した。
著者: Hao Zhang, Jiaming Chen, Jiyu Cheng, Yibin Li, Simon X. Yang, Wei Zhang
分類: cs.RO, cs.MA
原文アブストラクト
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.
