让机器人听懂自然语言指令，自动规划符合社交规范的路径
NORM-Nav: Zero-Shot Mobile Robot Navigation with Natural Language Behavioral Constraints
- 用大模型解析语言指令，生成多层成本地图
- 零样本下任务成功率提升，轨迹更贴近人类行为
- 适合需要社会交互的移动机器人场景
在以人为中心的环境中，移动机器人不仅需规划无碰撞路径，还需遵循本地行为规范。传统基于成本图的导航强调几何可行性，常忽略此类要求，导致社交不适宜行为。本文提出NORM-Nav，一种将自然语言行为约束融入成本图规划的零样本框架。大模型（LLM）解析每条指令为结构化约束，并结合实时视觉与激光雷达感知进行语义定位。这些约束被编码为包含几何、语义、方向和速度信息的多层成本图，可直接兼容标准栅格规划器。仿真与真实世界实验表明，NORM-Nav在任务成功率上优于代表性基线，生成的轨迹更接近人类参考轨迹。
原文摘要 · Abstract (English)
Mobile robots operating in human-centered environments must generate not only collision-free paths but also trajectories that follow local behavioral conventions. Conventional costmap-based navigation emphasizes geometric feasibility and often overlooks such requirements, which can result in socially inappropriate behaviors. This paper presents NORM-Nav, a zero-shot framework that integrates natural language behavioral constraints into costmap-based planning. An LLM parses each instruction into structured constraints and grounds them using real-time vision--LiDAR perception. These constraints are encoded as multi-layer costmaps that represent geometric, semantic, directional, and velocity cues and are directly compatible with standard grid-based planners. Simulation and real-world experiments indicate that NORM-Nav improves task success rates and produces trajectories closer to human references than representative baselines. The project website is available at https://ei-nav.github.io/NORM-Nav.
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