mlsanityData to 7 Oct 2026

KGLAMP: Knowledge Graph-guided Language model for Adaptive Multi-robot Planning and Replanning

arXiv 2602.04129 ↗First posted 4 Feb 2026cs.RO cs.AI cs.ETcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

35 of 45 references.

References · 35

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23 more
  1. 2024ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigationcited by 67
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teamscited by 1
  2. 2026Structured LLM Reasoning for Zero-Shot Human--Robot Coordination Under Hidden Goalscited by 0