Learning Semantic Priorities for Autonomous Target Search

Abstract

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.

Figures (1)

• Figure 3: Conceptual overview of the proposed framework. During data collection, an expert generates interventions into the planner's goal output, prioritizing certain semantically relevant objects (depicted by stars). These are used to train a semantic model, which outputs priorities for each exploration frontier that, in turn, guide the exploration planner. The exploration planner outputs the next frontier viewpoint to navigate to.