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The Chair of Cyber-Physical-Systems

The Chair of Cyber-Physical-Systems at the Montanuniversität Leoben in Austria is headed by Prof. Elmar Rueckert.

The group’s research topics are autonomous systems, machine and deep learning, embedded smart sensing systems, and computational models.

Find out more about us here  (a recent post in German).

Or have a look at this presentation of our research.

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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

December 21, 2018

Conference paper accepted at BIOSIGNALS 2019

Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E. (2019). Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors, Proceedings of the International Conference on Bio-inspired Systems and..Read More

October 9, 2018

Journal Paper Accepted at Neural Networks

Daniel Tanneberg, Jan Peters, Elmar Rueckert Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks accepted (Oct, 9th 2018) at Neural Networks – Elsevier with an Impact Factor..Read More

July 31, 2018

Conference paper accepted at VAILD 2018

Gondaliya, D. Kaushikkumar; Peters, J.; Rueckert, E. (2018). Learning to categorize bug reports with LSTM networks: An empirical study on thousands of real bug reports from a world leading software..Read More

July 18, 2018

Journal Paper Accepted at JMLR – Journal of Machine Learning Research.

Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling accepted (Oct, 8th 2018) at Journal of Machine Learning Research (JMLR).

February 1, 2018

1st day as Assistant Professor

September 18, 2017

Invited Talk at the ICDL Conference, Lisbon, Portugal

Home – Background Slideshow Title: Experience Replay and Intrinsic Motivation in Neural Motor Skill Learning Models

September 18, 2017

3 HUMANOIDS Papers Accepted

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

September 1, 2017

CoRL Paper accepted

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).

August 4, 2017

W1 Juniorprofessorship with tenure track at University Lübeck

With February 1st, 2018 I will work as professor for robotics at the university Lübeck.

February 28, 2017

Invited Talk at 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

January 31, 2017

Invited Talk at the Frankfurt Institute for Advanced Studies (FIAS), Germany

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

November 18, 2016

Invited Talk at the Institute of Neuroinformatics (INI), Zurich, Switzerland

Probabilistic computational models of human motor control for robot learning.

November 14, 2016

Invited Talk at the Albert-Ludwigs-Universität Freiburg, Germany

Neural models for brain-machine interfaces and anthropomorphic robotics

February 6, 2016

Journal Paper Accepted at Nature Publishing Group: Scientific Reports.

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.

December 18, 2015

Journal Paper Accepted at Nature Publishing Group: Scientific Reports.

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan Recurrent Spiking Networks Solve Planning Tasks Nature Publishing Group: Scientific Reports, 6 (21142), 2016.

More news on Professor Rueckert’s page.