让大模型当语义传感器，智能规划机器人安全路径
Bootstrapped LLM Semantics for Context-Aware Path Planning
- 用大模型生成多个危险判断，通过贝叶斯自助法估算风险后验
- 基于风险统计构建代价函数，实现对上下文的动态响应
- 适合需要理解语义环境的机器人导航场景
将自然语言提示用于机器人任务规划，长期聚焦于执行什么任务（如目标选择、技能排序），而非如何在富含语义的人类中心空间中安全高效地执行。本文提出一种框架，将大语言模型（LLM）转化为随机语义传感器，其输出用于调节经典规划器。给定提示和语义地图，我们从LLM中采样多个“危险”判断，利用贝叶斯自助法近似每类风险的后验分布。基于该后验的统计量，构建势能代价，形成路径规划问题。在仿真环境与基于BIM的数字孪生系统中，该方法能根据显式指令和隐式上下文信息动态调整机器人移动策略。实验展示了定性与定量结果。
原文摘要 · Abstract (English)
Prompting robots with natural language (NL) has largely been studied as what task to execute (goal selection, skill sequencing) rather than how to execute that task safely and efficiently in semantically rich, human-centric spaces. We address this gap with a framework that turns a large language model (LLM) into a stochastic semantic sensor whose outputs modulate a classical planner. Given a prompt and a semantic map, we draw multiple LLM "danger" judgments and apply a Bayesian bootstrap to approximate a posterior over per-class risk. Using statistics from the posterior, we create a potential cost to formulate a path planning problem. Across simulated environments and a BIM-backed digital twin, our method adapts how the robot moves in response to explicit prompts and implicit contextual information. We present qualitative and quantitative results.
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