用大模型让机器人团队自适应协作导航，效率提升超四成。
SayCoNav: Utilizing Large Language Models for Adaptive Collaboration in Decentralized Multi-Robot Navigation
- 用大语言模型生成动态协作策略，支持去中心化自主决策。
- 在未知环境中多目标搜索任务中，最高提升44.28%的搜寻效率。
- 适合异构机器人团队在复杂动态场景中执行协同任务。
自适应协作对自主机器人团队在大规模未知环境中的复杂导航任务至关重要。有效的协作策略应根据各机器人的能力与实时状态动态调整，以实现共同目标。本文提出SayCoNav，利用大语言模型（LLMs）自动生成机器人团队间的协作策略。基于该策略，每台机器人可独立使用LLM生成自身规划与行动；在导航过程中通过信息共享持续更新步骤化计划。我们在多目标导航（MultiON）任务上评估了SayCoNav，要求机器人团队利用互补优势在未知环境中高效搜寻多个不同物体。在不同团队配置与条件下与基线方法对比，实验结果表明，SayCoNav能通过异构机器人间的有效协作，将搜索效率最高提升44.28%，并可在任务执行中动态适应环境变化。
原文摘要 · Abstract (English)
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.
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