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Many multi-robot planning problems are burdened by the curse ofdimensionality, which compounds the difficulty of applying solutions tolarge-scale problem instances. The use of learning-based methods in multi-robotplanning holds great promise as it enables us to offload the onlinecomputational burden of expensive, yet optimal solvers, to an offline learningprocedure. Simply put, the idea is to train a policy to copy an optimal patterngenerated by a small-scale system, and then transfer that policy to much largersystems, in the hope that the learned strategy scales, while maintainingnear-optimal performance. Yet, a number of issues impede us from leveragingthis idea to its full potential. This blue-sky paper elaborates some of the keychallenges that remain.

The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts