用知识图谱增强大模型，让多机器人团队能自适应规划与重规划。
KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning
- 用知识图谱存储物体关系、可达性与机器人能力，指导大模型生成准确计划。
- 在MAT-THOR上性能比纯大模型和传统PDDL方法提升至少25.3%。
- 适合需要长期协作、环境动态变化的多机器人系统应用。
异构多机器人系统在长周期任务中日益普及，需协调不同能力进行协同规划。然而现有方法难以构建准确的符号化表示，且在动态环境中难以保持计划一致性。传统PDDL规划器依赖手工设计符号模型，而基于大模型的规划器常忽略机器人异质性与环境不确定性。本文提出KGLAMP，一种基于知识图谱引导的大模型多机器人规划框架。该框架维护一个结构化的知识图谱，编码物体关系、空间可达性及机器人能力，作为大模型生成准确PDDL问题规范的依据。知识图谱作为持久化、动态更新的记忆体，可融合新观测并检测不一致，触发重规划，使符号计划能适应环境变化。在MAT-THOR基准测试中，KGLAMP性能较纯大模型和基于PDDL的方法提升至少25.3%。
原文摘要 · Abstract (English)
Heterogeneous multi-robot systems are increasingly used in long-horizon missions requiring coordinated planning across diverse capabilities. However, existing planning approaches struggle to construct accurate symbolic representations and maintain plan consistency in dynamic environments. Classical PDDL planners require manually crafted symbolic models, while LLM-based planners often ignore agent heterogeneity and environmental uncertainty. We introduce KGLAMP, a knowledge-graph-guided LLM planning framework for heterogeneous multi-robot teams. The framework maintains a structured knowledge graph encoding object relations, spatial reachability, and robot capabilities, which guides the LLM in generating accurate PDDL problem specifications. The knowledge graph serves as a persistent, dynamically updated memory that incorporates new observations and triggers replanning upon detecting inconsistencies, enabling symbolic plans to adapt to evolving world states. Experiments on the MAT-THOR benchmark show that KGLAMP improves performance by at least 25.3% over both LLM-only and PDDL-based variants.
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