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

## TL;DR

This paper introduces a method for automatically constructing flexible yet explicative languages for task planning robots, enabling robust coordination with optimality guarantees by translating temporal-state constraints into language.

## Contribution

It presents a novel approach to reverse-engineer a language from temporal constraints, enhancing flexibility and coordination in robot task planning.

## Key findings

- The proposed language enables robust coordination among robots.
- The approach provides optimality guarantees in plan execution.
- Validation shows advantages in various scenarios.

## Abstract

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.