Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances. The use of learning-based methods in multi-robot planning holds great promise as it enables us to offload the online computational burden of expensive, yet optimal solvers, to an offline learning procedure. Simply put, the idea is to train a policy to copy an optimal pattern generated by a small-scale system, and then transfer that policy to much larger systems, in the hope that the learned strategy scales, while maintaining near-optimal performance. Yet, a number of issues impede us from leveraging this idea to its full potential. This blue-sky paper elaborates some of the key challenges that remain.

中文翻译：

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多机器人规划的圣杯：学习从离线优化专家那里生成在线可扩展的解决方案

许多多机器人规划问题都受到维数灾难的影响，这增加了将解决方案应用于大规模问题实例的难度。在多机器人规划中使用基于学习的方法具有很大的前景，因为它使我们能够将昂贵但最优的求解器的在线计算负担卸载到离线学习过程。简单地说，这个想法是训练一个策略来复制由小规模系统生成的最佳模式，然后将该策略转移到更大的系统，希望学到的策略可以扩展，同时保持接近最佳的性能。然而，许多问题阻碍我们充分发挥这一想法的潜力。这份充满希望的论文阐述了一些仍然存在的关键挑战。