# SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation

arXiv: https://arxiv.org/abs/2505.13729 · HTML: https://mlsanity.com/paper/2505.13729/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 23 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 20 of 20)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [L3MVN: Leveraging Large Language Models for Visual Target Navigation](https://mlsanity.com/paper/2304.05501/) (2023)
- [SG-Nav: Online 3D Scene Graph Prompting for LLM-based Zero-shot Object Navigation](https://mlsanity.com/paper/2410.08189/) (2024)
- [UniGoal: Towards Universal Zero-shot Goal-oriented Navigation](https://mlsanity.com/paper/2503.10630/) (2025)
- [SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments](https://mlsanity.com/paper/2309.04077/) (2023)
- [Learning Efficient Multi-Agent Cooperative Visual Exploration](https://mlsanity.com/paper/2110.05734/) (2021)
- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration](https://mlsanity.com/paper/2412.18292/) (2024)
- [MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2409.16030/) (2024)
- [Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey](https://mlsanity.com/paper/2301.11361/) (2023)
- [Sequence-Agnostic Multi-Object Navigation](https://mlsanity.com/paper/2305.06178/) (2023)
- [Open-Architecture End-to-End System for Real-World Autonomous Robot Navigation](https://mlsanity.com/paper/2410.06239/) (2024)
- [Graph2Nav: 3D Object-Relation Graph Generation to Robot Navigation](https://mlsanity.com/paper/2504.16782/) (2025)

## Built on by (most cited first, 5 of 5)

- [3D Scene Graphs: Open Challenges and Future Directions](https://mlsanity.com/paper/2606.19383/) (2026)
- [COMRES-VLM: Coordinated Multi-Robot Exploration and Search using Vision Language Models](https://mlsanity.com/paper/2509.26324/) (2025)
- [Does Peer Observation Help? Vision-Sharing Collaboration for Vision-Language Navigation](https://mlsanity.com/paper/2603.20804/) (2026)
- [Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method](https://mlsanity.com/paper/2609.08402/) (2026)
- [DC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams](https://mlsanity.com/paper/2604.03905/) (2026)

## Abstract (arXiv)

Adaptive collaboration is critical to a team of autonomous robots to perform complicated navigation tasks in large-scale unknown environments. An effective collaboration strategy should be determined and adapted according to each robot's skills and current status to successfully achieve the shared goal. We present SayCoNav, a new approach that leverages large language models (LLMs) for automatically generating this collaboration strategy among a team of robots. Building on the collaboration strategy, each robot uses the LLM to generate its plans and actions in a decentralized way. By sharing information to each other during navigation, each robot also continuously updates its step-by-step plans accordingly. We evaluate SayCoNav on Multi-Object Navigation (MultiON) tasks, that require the team of the robots to utilize their complementary strengths to efficiently search multiple different objects in unknown environments. By validating SayCoNav with varied team compositions and conditions against baseline methods, our experimental results show that SayCoNav can improve search efficiency by at most 44.28% through effective collaboration among heterogeneous robots. It can also dynamically adapt to the changing conditions during task execution.