AI for Science

AI for Scientific Discovery of Cognitive Control:

Gemeinsames Projekt mit der Princeton University finanziert durch den " Eric and Wendy Schmidt Transformative Technology Fund".

2026

A Scene Graph Backed Approach to Open Set Semantic Mapping
Martin Günther, Felix Igelbrink, Oscar Lima, Lennart Niecksch, Marian Renz, Martin Atzmueller, 2026
 doi.org/10.48550/arXiv.2602.03781

Interactive Open-Set Semantic Mapping with a 3D Scene Graph Backend
Felix Igelbrink, Lennart Niecksch, Martin Günther, Marian Renz, Oscar Lima, Martin Atzmueller, 
Accepted at: Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2026), August 15-21, Bremen, Germany
2026.

LIEREx: Language-Image Embeddings for Robotic Exploration
Felix Igelbrink, Lennart Niecksch, Marian Renz, Martin Günther, Martin Atzmueller, 2026
 doi.org/10.1007/s13218-026-00902-6

Agentic AI for Robot Control: Flexible but still Fragile
Oscar Lima, Marc Vinci, Martin Günther, Marian Renz, Alexander Sung, Sebastian Stock, Johannes Brust, Lennart Niecksch, Zongyao Yi, Felix Igelbrink, Benjamin Kisliuk, Martin Atzmueller, Joachim Hertzberg, 2026
 doi.org/10.48550/arXiv.2602.13081

Time series feature extraction and crop type classification on spatiotemporal soil sensor network data
David Massanés, Martin Atzmueller, 2026
 doi.org/10.18420/giljt2026_08

ExPrIS: Knowledge-Level Expectations as Priors for Object Interpretation from Sensor Data
Marian Renz, Martin Günther, Felix Igelbrink, Oscar Lima, Martin Atzmueller, 2026
 doi.org/10.1007/s13218-026-00901-7

Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel, Martin Atzmueller, Gerard B.M. Heuvelink, 2026
  doi.org/10.1016/j.compag.2025.111352

Auf dem Weg zu einer KI-gestützten Gewässer- und Wasseransammlungsdetektion zur adaptiven Steuerung von Pflanzenschutz- und Düngerapplikation auf Landmaschinen
Christoph Tieben, Robert Rettig, Benjamin Kisliuk, Andrej Lejman, Christoph Manss, Kai von Szadkowski, Martin Atzmueller, 46. GIL-Jahrestagung, Datenräume in der Land-, Forst- und Ernährungswirtschaft
pp. 446--451, 2026.

Towards Reconfigurability of Plan-Based Controllers Through Metacognition
Alessandro Umbrico, Sebastian Stock, Martin Atzmueller, Amedeo Cesta, Elisa Foderaro, Joachim Hertzberg, Oscar Lima, Juan Carlos Saborío, Marc Vinci, Nicola Pedrocchi, Andrea Orlandini, 2026
  doi.org/10.1007/978-981-95-2382-5_42

Improving transparency in machine learning for agriculture: a case study on mushroom classification using CNN and Grad-CAM
Xiao, Alina Griesel, Wei, Martin Atzmueller, 46. GIL-Jahrestagung, Datenräume in der Land-, Forst- und Ernährungswirtschaft pp. 176-187, 2026.

2025

 ProtoMask: Segmentation-Guided Prototype Learning. Steffen Meinert, Philipp Schlinge, Nils Strodthoff, Martin Atzmueller, 2025. 
 https://doi.org/10.48550/arXiv.2510.00683

 Comprehensive Evaluation of Prototype Neural Networks. Philipp Schlinge, Steffen Meinert, Martin Atzmueller. KI - Künstliche Intelligenz (2025)

 Evaluating Higher-Level and Symbolic Features in Deep Learning on Time Series: Towards Simpler Explainability. Schwenke, L., Stückemann, T., Atzmueller, M. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2025. Lecture Notes in Computer Science, vol 16008, S. 345-357. Springer, Cham.
doi.org/10.1007/978-3-032-02725-2_27