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In this paper, we consider a first step to bridge a gap in coordinating taskplanning robots. Specifically, we study the automatic construction of languagesthat are maximally flexible while being sufficiently explicative forcoordination. To this end, we view language as a machinery for specifyingtemporal-state constraints of plans. Such a view enables us to reverse-engineera language from the ground up by mapping these composable constraints to words.Our language expresses a plan for any given task as a "plan sketch" to conveyjust-enough details while maximizing the flexibility to realize it, leading torobust coordination with optimality guarantees among other benefits. Weformulate and analyze the problem, provide an approximate solution, andvalidate the advantages of our approach under various scenarios to shed lighton its applications.

From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots