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This paper introduces a method for autonomous target search that learns semantic priorities from simulated expert guidance to improve efficiency in unknown environments. A semantic priority model is trained on synthetic datasets of expert demonstrations and then integrated into a frontier exploration planner using combinatorial optimization. Results in unseen environments demonstrate faster target recovery compared to a standard coverage-driven exploration planner, showcasing the value of learned semantic priors.
Forget exhaustive coverage – robots can find targets faster by learning what matters most from expert search strategies.
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.
