用图神经网络增强大模型，让机器人实时感知风险并动态调整任务。
Graphormer-Guided Task Planning: Beyond Static Rules with LLM Safety Perception
- 构建动态时空安全图，融合空间与上下文风险因素
- 在AI2-THOR中风险检测准确率提升，适应性更强
- 适合需要实时安全响应的机器人应用场景
大型语言模型（LLM）在机器人任务规划中的应用日益广泛，但其确保任务安全执行的能力仍不充分。现有方法缺乏结构化风险感知能力，在要求低延迟危险应对的安全关键场景中表现不足。为此，我们提出一种基于Graphormer的风险感知任务规划框架，将LLM决策与结构化安全建模结合。该框架构建动态的时空语义安全图，捕捉空间与上下文风险因素，实现在线危险检测与任务自适应优化。不同于依赖预设安全规则的方法，本框架引入上下文感知的风险感知模块，根据实时任务执行持续更新安全预测，实现超越静态规则的灵活可扩展安全合规。在AI2-THOR环境中的实验表明，相较于静态规则和仅使用LLM的基线方法，本框架在连续环境中显著提升了风险检测准确率、安全预警能力与任务适应性。
原文摘要 · Abstract (English)
Recent advancements in large language models (LLMs) have expanded their role in robotic task planning. However, while LLMs have been explored for generating feasible task sequences, their ability to ensure safe task execution remains underdeveloped. Existing methods struggle with structured risk perception, making them inadequate for safety-critical applications where low-latency hazard adaptation is required. To address this limitation, we propose a Graphormer-enhanced risk-aware task planning framework that combines LLM-based decision-making with structured safety modeling. Our approach constructs a dynamic spatio-semantic safety graph, capturing spatial and contextual risk factors to enable online hazard detection and adaptive task refinement. Unlike existing methods that rely on predefined safety constraints, our framework introduces a context-aware risk perception module that continuously refines safety predictions based on real-time task execution. This enables a more flexible and scalable approach to robotic planning, allowing for adaptive safety compliance beyond static rules. To validate our framework, we conduct experiments in the AI2-THOR environment. The experiments results validates improvements in risk detection accuracy, rising safety notice, and task adaptability of our framework in continuous environments compared to static rule-based and LLM-only baselines. Our project is available at https://github.com/hwj20/GGTP
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