Learning Semantic Priorities for Autonomous Target Search

2026-03-31

AI Summary

This work addresses the limited generalization of existing object search methods, which rely heavily on large amounts of in-domain training data and struggle in unseen environments. To overcome this challenge, the authors propose an autonomous search framework that integrates semantic priors derived from human experts. Specifically, they model semantic priorities by simulating expert guidance and embed this model into a combinatorial optimization–based frontier exploration planner. This approach is the first to incorporate high-level semantic relationships into object search, significantly enhancing both generalization capability and search efficiency in novel environments. Simulation experiments demonstrate that, compared to purely coverage-driven baseline methods, the proposed framework achieves full environmental coverage while substantially accelerating target discovery.

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.

Problem

• semantic features
• autonomous target search
• unknown environments
• expert knowledge
• search and rescue

Innovation

• semantic priorities
• expert-guided learning
• frontier exploration
• combinatorial optimization
• autonomous target search