Learning Semantic Priorities for Autonomous Target Search
This work addresses the need for adaptable robotic search and rescue in diverse environments, though it is incremental as it builds on existing frontier exploration methods.
The paper tackles the problem of inefficient target search in unknown environments by leveraging expert input to train a semantic priority model, resulting in faster target recovery compared to a coverage-driven planner in simulations.
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.