From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots
This addresses coordination challenges in multi-robot systems, though it appears incremental as it builds on existing planning and language frameworks.
The paper tackles the problem of coordinating task planning robots by automatically constructing languages that balance flexibility and explicitness, resulting in robust coordination with optimality guarantees.
In this paper, we consider a first step to bridge a gap in coordinating task planning robots. Specifically, we study the automatic construction of languages that are maximally flexible while being sufficiently explicative for coordination. To this end, we view language as a machinery for specifying temporal-state constraints of plans. Such a view enables us to reverse-engineer a 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 convey just-enough details while maximizing the flexibility to realize it, leading to robust coordination with optimality guarantees among other benefits. We formulate and analyze the problem, provide an approximate solution, and validate the advantages of our approach under various scenarios to shed light on its applications.