用大模型当传感器，让机器人根据施工指令自动规划更安全的路径。
Digital Twin-Guided Robot Path Planning: A Beta-Bernoulli Fusion with Large Language Model as a Sensor
- 把大模型输出的危险评分当作伪计数，更新障碍物的排斥系数
- 路径规划的代价函数融合了上下文感知的连续排斥增益
- 适合需要理解自然语言指令的施工机器人系统
近年来，将自然语言提示融入机器人任务规划受到广泛关注。在建筑领域，建筑信息模型（BIM）包含丰富的环境自然语言描述。本文提出一种新框架，通过贝塔-伯努利贝叶斯融合，将自然语言指令与基于BIM的语义地图结合：将每个障碍物的设计期排斥系数视为贝塔分布随机变量，大模型返回的危险评分作为伪计数，用于更新贝塔分布参数α和β。后验均值得到连续、上下文感知的排斥增益，用于增强基于欧氏距离的势场代价启发式。通过根据用户指令中的情感与上下文调整排斥增益，该方法引导机器人选择更安全、更符合语境的路径。该方法数值稳定，可串联多个自然语言命令，支持施工人员或工头输入，并可灵活集成于任何学习型或经典AI框架中。仿真结果表明，该贝塔-伯努利融合在路径鲁棒性与有效性上均实现定性和定量提升。
原文摘要 · Abstract (English)
Integrating natural language (NL) prompts into robotic mission planning has attracted significant interest in recent years. In the construction domain, Building Information Models (BIM) encapsulate rich NL descriptions of the environment. We present a novel framework that fuses NL directives with BIM-derived semantic maps via a Beta-Bernoulli Bayesian fusion by interpreting the LLM as a sensor: each obstacle's design-time repulsive coefficient is treated as a Beta(alpha, beta) random variable and LLM-returned danger scores are incorporated as pseudo-counts to update alpha and beta. The resulting posterior mean yields a continuous, context-aware repulsive gain that augments a Euclidean-distance-based potential field for cost heuristics. By adjusting gains based on sentiment and context inferred from user prompts, our method guides robots along safer, more context-aware paths. This provides a numerically stable method that can chain multiple natural commands and prompts from construction workers and foreman to enable planning while giving flexibility to be integrated in any learned or classical AI framework. Simulation results demonstrate that this Beta-Bernoulli fusion yields both qualitative and quantitative improvements in path robustness and validity.
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