SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation (2505.13729v1)

Abstract: 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 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.

SayCoNav: A New Approach for Multi-Robot Navigation Using LLMs

The paper "SayCoNav: Utilizing LLMs for Adaptive Collaboration in Decentralized Multi-Robot Navigation" presents a novel framework aimed at enhancing the synergy among autonomous robots by leveraging LLMs. The core focus lies in addressing complex navigation tasks across large-scale, unknown environments where adaptive collaboration becomes imperative for task success and efficiency.

Key Contributions and Methodology

SayCoNav introduces a decentralized planning architecture that uses LLMs to facilitate seamless collaboration among heterogeneous robots. The framework encompasses three essential planning levels: global, local, and action planners. The top-level global planner utilizes LLMs to generate a collaboration strategy based on the skill specifications of each robot prior to task execution. This strategy ensures optimal allocation of roles and coordination mechanisms among robots, leveraging their diverse skill sets to accomplish shared objectives efficiently.

To accommodate dynamic conditions, SayCoNav's global planner is capable of regenerating the collaboration strategy when the physical condition of any robot changes during the mission. The middle-level local planner then generates step-by-step plans from the collaboration strategy using a dynamically evolving prompt system enriched with feedback, task summaries, local scene graphs, and inter-robot communication messages. This ensures continuous updating of plans based on real-time changes and shared information, enabling decentralized task execution across unknown environments.

Experimental Validation

The efficacy of SayCoNav is evaluated through a series of experiments employed using the ProcTHOR framework, focusing on Multi-Object Navigation (MultiON) tasks requiring advanced collaboration. The results are compelling, demonstrating up to 44.28% improvement in search efficiency compared to baseline methods. Such enhancement in performance is observed in varied team compositions, from homogeneous to heterogeneous robots, highlighting SayCoNav's versatile application under different skill settings.

Implications and Future Directions

SayCoNav heralds important implications for decentralized robotic systems, often constrained by centralized architectures or pre-defined distributed processes, which lack flexibility in real-world scenarios. By enabling automatic generation and dynamic adaptation of collaboration strategies, SayCoNav significantly advances the capabilities of multi-robot systems.

The introduction of LLMs in this domain offers potential future directions for AI-enhanced robotic autonomy. While current limitations include handling complex collaborative tasks beyond LLM-generated strategies, future work may expand on refining LLM architectures to better process intricate cooperative demands or extend to real-world implementations beyond simulated environments.

Overall, the paper contributes meaningfully to the field of multi-robot navigation, setting a foundation for further exploration into adaptive collaborative methodologies powered by LLMs.