RoCo: Dialectic Multi-Robot Collaboration with Large Language Models

• URL: http://arxiv.org/abs/2307.04738v1
• Date: Mon, 10 Jul 2023 17:52:01 GMT
• Title: RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
• Authors: Zhao Mandi, Shreeya Jain, Shuran Song
• Abstract summary: We propose a novel approach to multi-robot collaboration that harnesses the power of pre-trained large language models (LLMs) We show RoCo easily incorporates human-in-the-loop, where a user can communicate and collaborate with a robot agent to complete tasks together.
• Score: 13.260289557301688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel approach to multi-robot collaboration that harnesses the power of pre-trained large language models (LLMs) for both high-level communication and low-level path planning. Robots are equipped with LLMs to discuss and collectively reason task strategies. They then generate sub-task plans and task space waypoint paths, which are used by a multi-arm motion planner to accelerate trajectory planning. We also provide feedback from the environment, such as collision checking, and prompt the LLM agents to improve their plan and waypoints in-context. For evaluation, we introduce RoCoBench, a 6-task benchmark covering a wide range of multi-robot collaboration scenarios, accompanied by a text-only dataset for agent representation and reasoning. We experimentally demonstrate the effectiveness of our approach -- it achieves high success rates across all tasks in RoCoBench and adapts to variations in task semantics. Our dialog setup offers high interpretability and flexibility -- in real world experiments, we show RoCo easily incorporates human-in-the-loop, where a user can communicate and collaborate with a robot agent to complete tasks together. See project website https://project-roco.github.io for videos and code.

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