Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation using Large Language Models

• URL: http://arxiv.org/abs/2310.07937v2
• Date: Mon, 25 Dec 2023 07:57:13 GMT
• Title: Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation using Large Language Models
• Authors: Bangguo Yu, Hamidreza Kasaei, Ming Cao
• Abstract summary: Co-NavGPT is an innovative framework that integrates Large Language Models as a global planner for multi-robot cooperative visual target navigation. It encodes the explored environment data into prompts, enhancing LLMs' scene comprehension. It then assigns exploration frontiers to each robot for efficient target search.
• Score: 10.312968200748118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In advanced human-robot interaction tasks, visual target navigation is crucial for autonomous robots navigating unknown environments. While numerous approaches have been developed in the past, most are designed for single-robot operations, which often suffer from reduced efficiency and robustness due to environmental complexities. Furthermore, learning policies for multi-robot collaboration are resource-intensive. To address these challenges, we propose Co-NavGPT, an innovative framework that integrates Large Language Models (LLMs) as a global planner for multi-robot cooperative visual target navigation. Co-NavGPT encodes the explored environment data into prompts, enhancing LLMs' scene comprehension. It then assigns exploration frontiers to each robot for efficient target search. Experimental results on Habitat-Matterport 3D (HM3D) demonstrate that Co-NavGPT surpasses existing models in success rates and efficiency without any learning process, demonstrating the vast potential of LLMs in multi-robot collaboration domains. The supplementary video, prompts, and code can be accessed via the following link: https://sites.google.com/view/co-navgpt

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