Online Resynthesis of High-Level Collaborative Tasks for Robots with Changing Capabilities

TL;DR

This paper presents a framework for dynamically resynthesizing high-level robot team behaviors in response to changes in individual robot capabilities, ensuring task satisfaction with minimal reassignments.

Contribution

It introduces a method to automatically adjust robot behaviors at runtime using an extended LTL^ ext{ extpsi} logic, accommodating robot failures and capabilities changes.

Findings

Effective in simulated warehouse scenarios

Minimizes reassignments during robot capability changes

Enhances expressivity of task specifications

Abstract

Given a collaborative high-level task and a team of heterogeneous robots and behaviors to satisfy it, this work focuses on the challenge of automatically, at runtime, adjusting the individual robot behaviors such that the task is still satisfied, when robots encounter changes to their abilities--either failures or additional actions they can perform. We consider tasks encoded in LTL^\psi and minimize global teaming reassignments (and as a result, local resynthesis) when robots' capabilities change. We also increase the expressivity of LTL^\psi by including additional types of constraints on the overall teaming assignment that the user can specify, such as the minimum number of robots required for each assignment. We demonstrate the framework in a simulated warehouse scenario.