Our paper by Vedant Dave, Christian Rauch and Elmar Rueckert  on “Task-Relevant Language-conditioned Segmentation for Robust Generalization in Reinforcement Learning” was accepted for publication at the Transactions on Machine Learning Research (TMLR) in July 2026.

We are pleased to announce that our EU-funded Doctoral Network project has been successfully approved.

The project offers an exciting opportunity for doctoral candidates to gain both academic and industrial experience, including 18 months at Siemens in Graz and 18 months at the Chair of Cyber-Physical Systems (CPS) in Leoben.

This unique setup enables close collaboration between research and industry, fostering impactful work at the intersection of machine learning, robotics, and real-world applications.

More information: https://www.reffracteur.eu

The paper by Fotios Lygerakis, Ozan Özdenizci, and Elmar Rueckert titled ‘ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers’ has been accepted for publication at TMLR (Transactions on Machine Learning Research) in April 2026.

The work introduces a novel approach for aligning visual and tactile information in multimodal transformer architectures, enabling more effective cross-modal learning.

You can access the paper and reviews here: https://openreview.net/forum?id=mxzzO66Zbu

Our proposal RoboWork—an industrial research initiative focused on advancing humanoid robotics for real-world applications—has been accepted for funding!

Over the coming years, the project will drive knowledge creation, development, and evaluation of key technologies that are not yet sufficiently explored or translated into industrial-grade solutions.

RoboWork aims to unlock the full potential of humanoid robots in industrial environments, paving the way for their effective and reliable deployment in future workplaces.

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 Sahar Keshavarz , Asad Elmgerbi , Vedant Dave, Elmar Rückert, and Gerhard Thonhauser, 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.