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This paper introduces a method for autonomous target search that learns semantic priorities from simulated expert guidance. A semantic priority model is trained on synthetic datasets of expert demonstrations, then used within a frontier exploration planner to guide search. Experiments in unseen environments demonstrate faster target recovery compared to coverage-driven exploration, showcasing the benefits of incorporating semantic knowledge.
Forget brute-force coverage – this method learns from simulated expert guidance to prioritize semantically relevant areas, dramatically speeding up target search in unseen environments.
The use of semantic features can improve the efficiency of target search in unknown environments for robotic search and rescue missions. Current target search methods rely on training with large datasets of similar domains, which limits the adaptability to diverse environments. However, human experts possess high-level knowledge about semantic relationships necessary to effectively guide a robot during target search missions in diverse and previously unseen environments. In this paper, we propose a target search method that leverages expert input to train a model of semantic priorities. By employing the learned priorities in a frontier exploration planner using combinatorial optimization, our approach achieves efficient target search driven by semantic features while ensuring robustness and complete coverage. The proposed semantic priority model is trained with several synthetic datasets of simulated expert guidance for target search. Simulation tests in previously unseen environments show that our method consistently achieves faster target recovery than a coverage-driven exploration planner.
