Sentinel：具身协同空间推理与规划
Sentinel: Embodied Cooperative Spatial Reasoning and Planning
AI总结：
提出Sentinel挑战和CoSaR框架，通过自然语言通信与空间导航算法结合，解决多智能体在城市规模户外环境中的协同空间推理与规划问题。
摘要：
在这项工作中，我们研究了协同空间智能，即分散的具身智能体在跨城市规模的户外领域中，在动态环境约束下有效协调的能力。我们引入了Sentinel挑战，这是一个基准测试，其中多个分散的具身智能体必须通过自然语言进行通信，以在大规模城市户外环境中就一个相互安全且方便的会合点达成一致。然后，每个智能体必须安全导航，同时避开巡逻的动态哨兵，并使用提供粗略空间信息的工具。为了解决这个问题，我们提出了CoSaR（协同空间推理与规划）框架，该框架将基础模型的高层通信和规划能力与经典空间导航算法的精度相结合。CoSaR使智能体能够交换情境更新、推理不断变化的空间约束，并协同重新规划轨迹。在14个城市级别场景（包含3-5个智能体）的评估中，CoSaR始终导致更快的聚集、更短的路径长度和更高的安全性。我们的结果表明，将动态通信与空间推理相结合对于鲁棒的多智能体协作至关重要。通过形式化这一新设置并提供可扩展的基准测试，我们旨在为推进具身多智能体系统中的协同空间智能奠定基础。代码和挑战可在https://github.com/UMass-Embodied-AGI/Sentinel获取。
英文摘要：
In this work, we study Cooperative Spatial Intelligence, the ability of decentralized embodied agents to coordinate effectively under dynamic environmental constraints across city-scale outdoor domains. We introduce Sentinel Challenge, a benchmark where multiple decentralized embodied agents must communicate in natural language to agree on a mutually safe and convenient meeting point within large, city-scale outdoor environments. Each agent must then navigate safely while avoiding dynamic sentinels patrolling the area, using a tool that provides coarse spatial information. To address this, we propose CoSaR (Cooperative Spatial Reasoning and Planning), a framework that bridges the high-level communication and planning abilities of foundation models with the precision of classical spatial navigation algorithms. CoSaR enables agents to exchange situational updates, reason over evolving spatial constraints, and collaboratively replan trajectories. Evaluated across 14 city-level scenes with 3-5 agents, CoSaR consistently leads to faster gathering, shorter path lengths, and improved safety. Our results demonstrate that integrating dynamic communication with spatial reasoning is essential for robust multi-agent cooperation. By formalizing this new setting and providing a scalable benchmark, we aim to build a foundation for advancing cooperative spatial intelligence in embodied multi-agent systems. Code and challenge are available at https://github.com/UMass-Embodied-AGI/Sentinel.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
