ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data
Abstract
While deep learning has significantly advanced robotic object recognition, purely data-driven approaches often lack semantic consistency and fail to leverage valuable, pre-existing knowledge about the environment. This report presents the ExPrIS project, which addresses this challenge by investigating how knowledge-level expectations can serve as to improve object interpretation from sensor data. Our approach is based on the incremental construction of a 3D Semantic Scene Graph (3DSSG). We integrate expectations from two sources: contextual priors from past observations and semantic knowledge from external graphs like ConceptNet. These are embedded into a heterogeneous Graph Neural Network (GNN) to create an expectation-biased inference process. This method moves beyond static, frame-by-frame analysis to enhance the robustness and consistency of scene understanding over time. The report details this architecture, its evaluation, and outlines its planned integration on a mobile robotic platform.
Cite
@article{arxiv.2601.15025,
title = {ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data},
author = {Marian Renz and Martin Günther and Felix Igelbrink and Oscar Lima and Martin Atzmueller},
journal= {arXiv preprint arXiv:2601.15025},
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/s13218-026-00901-7