COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models

TL;DR

COHERENT introduces a novel LLM-based framework for heterogeneous multi-robot collaboration, enabling complex long-horizon task planning through a Proposal-Execution-Feedback-Adjustment mechanism, significantly improving success rates and efficiency.

Contribution

This work presents the first LLM-based framework for heterogeneous multi-robot collaboration with a new PEFA mechanism and a challenging benchmark for complex tasks.

Findings

Outperforms previous methods in success rate and efficiency

Successfully coordinates diverse robots like quadrotors, robotic dogs, and arms

Provides a new benchmark for complex multi-robot task planning

Abstract

Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results. However, they mainly focus on single or multiple homogeneous robots on simple tasks. Practically, complex long-horizon tasks always require collaboration among multiple heterogeneous robots especially with more complex action spaces, which makes these tasks more challenging. To this end, we propose COHERENT, a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems including quadrotors, robotic dogs, and robotic arms. Specifically, a Proposal-Execution-Feedback-Adjustment (PEFA) mechanism is designed to decompose and assign actions for individual robots, where a centralized task assigner makes a task planning proposal to decompose the complex task into subtasks, and then assigns subtasks to robot executors. Each robot executor selects a feasible action to implement the assigned subtask and reports self-reflection feedback to the task assigner for plan adjustment. The PEFA loops until the task is completed. Moreover, we create a challenging heterogeneous multi-robot task planning benchmark encompassing 100 complex long-horizon tasks. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency. The experimental videos, code, and benchmark are released at https://github.com/MrKeee/COHERENT.