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

TL;DR

This paper introduces a new benchmark environment for evaluating multi-robot collaboration in a target trapping scenario, emphasizing the importance of environment utilization and collaborative strategies.

Contribution

The paper presents a novel benchmark for multi-robot collaboration in Target Trapping Environment and evaluates multiple learning-based baselines.

Findings

Multiple learning-based baselines evaluated

Insights into collaboration regimes obtained

Benchmark publicly available for research use

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.