Multi-Robot Learning-Informed Task Planning Under Uncertainty

TL;DR

This paper introduces a multi-robot planning framework that combines learning and model-based planning to efficiently coordinate teams under environmental uncertainty, demonstrated in simulated and real household environments.

Contribution

It presents a novel multi-robot planning abstraction that integrates learning with model-based planning for uncertain environments, enabling effective long-horizon coordination.

Findings

Outperforms baseline methods in large ProcTHOR environments

Effective multi-stage planning for 1-3 robot teams

Successful real-world deployment with LoCoBot robots

Abstract

We want a multi-robot team to complete complex tasks in minimum time where the locations of task-relevant objects are not known. Effective task completion requires reasoning over long horizons about the likely locations of task-relevant objects, how individual actions contribute to overall progress, and how to coordinate team efforts. Planning in this setting is extremely challenging: even when task-relevant information is partially known, coordinating which robot performs which action and when is difficult, and uncertainty introduces a multiplicity of possible outcomes for each action, which further complicates long-horizon decision-making and coordination. To address this, we propose a multi-robot planning abstraction that integrates learning to estimate uncertain aspects of the environment with model-based planning for long-horizon coordination. We demonstrate the efficient multi-stage task planning of our approach for 1, 2, and 3 robot teams over competitive baselines in large ProcTHOR household environments. Additionally, we demonstrate the effectiveness of our approach with a team of two LoCoBot mobile robots in real household settings.