Semantic maps allow a robot to reason about its surroundings to fulfill tasks such as navigating known environments, finding specific objects, and exploring unmapped areas. Traditional mapping approaches provide accurate geometric representations but are often constrained by pre-designed symbolic vocabularies. The reliance on fixed object classes makes it impractical to handle out-of-distribution knowledge not defined at design time. Recent advances in Vision-Language Foundation Models, such as CLIP, enable open-set mapping, where objects are encoded as high-dimensional embeddings rather than fixed labels. In LIEREx, we integrate these VLFMs with established 3D Semantic Scene Graphs to enable target-directed exploration by an autonomous agent in partially unknown environments.
@article{arxiv.2602.01930,
title = {LIEREx: Language-Image Embeddings for Robotic Exploration},
author = {Felix Igelbrink and Lennart Niecksch and Marian Renz and Martin Günther and Martin Atzmueller},
journal= {arXiv preprint arXiv:2602.01930},
year = {2026}
}
Comments
This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this article is published in KI - K\"unstliche Intelligenz, and is available online at https://doi.org/10.1007/s13218-026-00902-6