SAGR builds a semantic area graph from occupancy maps so LLMs can assign rooms to robots for language-guided search, staying competitive with standard exploration while improving semantic target finding by up to 18.8% in large environments.
Coordinated multi-robot exploration,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PRoID uses learned map predictions to compute each robot's expected information delivery rate and triggers relay when immediate return is better, with a failure-aware variant that biases toward earlier returns.
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Semantic Area Graph Reasoning for Multi-Robot Language-Guided Search
SAGR builds a semantic area graph from occupancy maps so LLMs can assign rooms to robots for language-guided search, staying competitive with standard exploration while improving semantic target finding by up to 18.8% in large environments.
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PRoID: Predicted Rate of Information Delivery in Multi-Robot Exploration and Relaying
PRoID uses learned map predictions to compute each robot's expected information delivery rate and triggers relay when immediate return is better, with a failure-aware variant that biases toward earlier returns.