We are pleased to announce that our latest paper, ‘Prediction of feed flowrates, based on vibration patterns, generated by vibration sensors on an industrial circular vibrating screen using a set of ML models‘ by Philip Krukenfellner, Elmar Rueckert and Helmut Flachberger, has been published in the journal Minerals Engineering (Impact Factor 5.0 2025).
We are pleased to announce that our latest paper, “Deep reinforcement learning for automated decision-making in wellbore construction”, by , , Vedant Dave, Elmar Rückert, and , has been published in the journal Energy Reports (Impact Factor 5.1 2025).
We are pleased to announce that our latest paper, “Instance segmentation pipeline for etch pit detection and prismatic slip characterization on silicon carbide substrates”, by Georg Holub, Sebastian Hofer, Thomas Obermüller, Elmar Rückert, and Lorenz Romaner, has been published in the journal Engineering Applications of Artificial Intelligence (Impact Factor 8.0, 2025).
Our paper by Vedant Dave, Ozan Özdenizci and Elmar Rueckert on “Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling” was accepted for publication at the Transactions on Machine Learning Research (TMLR) in August 2025.
Our paper by Marko Jamsek, Elmar Rueckert and Jan Babic on ‘Foot Placement Prediction in Real-Time Using Probabilistic Movement Primitives‘ was accepted at the IEEE-RAS International Conference on Humanoid Robots 2025.
Our proposal MINEView—an initiative focused on autonomous systems for assessing underground mining conditions and delivering early warnings—has been accepted for funding!
Over the next three years, the CPS team will develop cutting-edge technologies for autonomous navigation, SLAM, condition monitoring, inspection, and AI-based inference in challenging underground environments.
Our joint grant proposal with Prof. Thomas Thurner was selected for funding by our university rectorate.
We will set up an Innovation lab for automation, robotics, and AI with
For more details see the project page.
The paper by Melanie Neubauer and Ozan Özdenizci and Justus Piater and Elmar Rueckert on Sparsifying instance segmentation models for efficient vision-based industrial recycling was selected for publication at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML 2025).
Our paper by Linus Ebere Nwankwo, Björn Ellensohn, Vedant Dave, Peter Hofer, Jan Forstner, Marlene Villneuve, Robert Galler, and Elmar Rueckert on ‘EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments‘ was accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA 2025).
Our paper by Ozan Özdenizci, Elmar Rueckert and Robert Legenstein on ‘Privacy-Aware Lifelong Learning’ was accepted for publication at the International Conference on Learning Representations (ICLR 2025).