Publikation
LIEREx: Language-Image Embeddings for Robotic Exploration
Felix Igelbrink; Lennart Niecksch; Marian Renz; Martin Günther; Martin Atzmueller
In: Lars Kunze (Hrsg.). KI - Künstliche Intelligenz, German Journal on Artificial Intelligence - Organ des Fachbereiches "Künstliche Intelligenz" der Gesellschaft für Informatik e.V. (KI), Vol. 01, Pages 1-7, Springer, 1/2026.
Zusammenfassung
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.