The Chair of Cyber-Physical-Systems

The Chair of Cyber-Physical Systems at the Technical University Leoben, led by Prof. Dr. Elmar Rueckert, conducts research at the intersection of artificial intelligence and autonomous systems.
Our work focuses on developing foundation models for robotics and exploring robot skill learning, including dexterous and visual–tactile manipulation, reinforcement learning, self-supervised, active / interactive, and intrinsically motivated learning. We are particularly interested in inference and reasoning mechanisms that enable robots to generalize and adapt across complex environments.
Application domains include humanoid robotics and autonomous systems for real-world tasks, industrial production, recycling, and mining, where our goal is to advance safety, efficiency, and sustainability through intelligent, adaptable robotic solutions.
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AI & Robotics Positions and Topics
The chair is offering a number of open positions and student thesis topics in AI and robotics.
Also check our wiki, which offers numerous public posts on open source code repositories or tutorials.
Latest news
News
http://www.e-fai.org/ Title: Experience Replay and Intrinsic Motivation in Neural Motor Skill Learning Models
Rueckert, E.; Nakatenus, M.; Tosatto, S.; Peters, J. (2017). Learning Inverse Dynamics Models in O(n) time with LSTM networks. Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with…Read More
Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals, Proceedings of the Conference on Robot Learning (CoRL).
With February 1st, 2018 I will work as professor for robotics at the university Lübeck.
Title: Neural models for robot motor skill learning. Abstract: The challenges in understanding human motor control, in brain-machine interfaces and anthropomorphic robotics are currently converging. Modern anthropomorphic robots with their compliant…Read More
Learning to Plan through Reinforcement Learning in Spiking Neural Networks Abstract: Movement planing is a fundamental skill that is involved in many human motor control tasks. While the hippocampus plays a…Read More
Probabilistic computational models of human motor control for robot learning.
Neural models for brain-machine interfaces and anthropomorphic robotics
Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Nature Publishing Group: Scientific Reports, 6 (28455), 2016.
Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan Recurrent Spiking Networks Solve Planning Tasks Nature Publishing Group: Scientific Reports, 6 (21142), 2016.
Elmar Rueckert joined the Autonomous Systems Labs of Prof. Jan Peters as Post-Doc in March 2014.
At the Technical University Graz, Austria with Prof. Wolfgang Maass.
Rueckert, Elmar; d’Avella, Andrea Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems Rueckert, Elmar; Neumann, Gerhard; Toussaint, Marc; Maass, Wolfgang Learned graphical models for…Read More
At the technical University Graz with Prof. Horst Bischof.
More news on Professor Rueckert’s page.