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University of Osnabrück

Academic institutioneurope · de
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Research library84linked papers
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Selected work

Representative Papers

LIEREx: Language-Image Embeddings for Robotic Exploration

Jan 30, 2026KI - Künstliche Intelligenz

This work addresses the limitation of traditional semantic mapping, which relies on predefined object categories and struggles to handle unknown objects, thereby hindering goal-directed exploration in partially unknown environments. To overcome this, the authors propose an open-vocabulary semantic mapping approach that integrates vision-language foundation models—such as CLIP—with 3D semantic scene graphs. This method introduces open-vocabulary semantic embeddings into 3D scene graph construction for the first time, effectively bypassing the constraints of fixed taxonomies. By enabling natural language–guided exploration strategies, the framework facilitates robust recognition and semantic reasoning about out-of-distribution target objects, significantly enhancing the robot’s semantic understanding and task generalization capabilities in dynamic and unfamiliar settings.

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ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data

Jan 21, 2026KI - Künstliche Intelligenz

This work proposes an expectation-guided dynamic semantic scene understanding framework to address the lack of semantic consistency in existing purely data-driven robotic object recognition methods, which struggle to incorporate environmental priors. By constructing a 3D semantic scene graph that integrates contextual priors with external knowledge bases such as ConceptNet, the framework introduces knowledge-level expectations as a prior for interpreting sensor data. Object reasoning is performed within a heterogeneous graph neural network, leveraging these knowledge-driven expectations to guide perception. The approach significantly enhances both semantic consistency and temporal continuity of object recognition in dynamic environments, enabling robots to interpret scenes more coherently over time.

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Recent publications

Latest Papers

Investigating Multimodal Informativity under Different Partner Visibility Conditions in Video-Mediated Dialogue

Aug 09, 2026

This study investigates how interactional visibility modulates the informational contributions of gesture and speech in referential tasks within video-mediated dialogue. By constructing models based on speech transcripts, gesture skeleton sequences, and their multimodal fusion—trained with alignment between representations and referential images—the research systematically evaluates the referential efficacy of each modality under varying visibility conditions. Findings demonstrate that gestures possess independent referential capacity, and multimodal fusion yields the greatest performance gains when speech is highly ambiguous. Moreover, human behavioral analyses reveal that visibility not only regulates the informativeness of gestural production but also elicits cross-turn verbal coordination, underscoring the critical role of pragmatic factors in multimodal reference.

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