Univ.-Prof. Dr. Elmar Rueckert

Chair of Cyber-Physical-Systems

Portrait Prof. Dr. Rueckert Elmar, January 2018

Short bio: Since March 2021 is Univ.-Prof. Dr. Elmar Rueckert the chair of the Cyber-Physical-Systems Institute at the Montanuniversität Leoben in Austria. He received his PhD in computer science at the Graz University of Technology in 2014 and worked for four years as senior researcher and research group leader at the Technical University of Darmstadt. Thereafter, he worked for three years as assistant professor at the University of Lübeck. His research interests include stochastic machine and deep learning, robotics and reinforcement learning and human motor control. In 2019, he was awarded with the ‘German Young Researcher Award’. 

Research Interests

  • Computational Modeling & Process Informatics: Cyber-Physical-Systems, Process Modeling in Metal Forming, Movement Decoding and Understanding, Brain- Computer-Interfaces, Electroencephalography, Spiking Neural Networks, Optimal Feedback Control, Muscle Synergies, Probabilistic Time-Series Models.
  • Machine & Deep Learning: Deep Networks, Graphical Models, Probabilistic Inference, Variational Inference, Gaussian Processes, Transfer Learning, Message Passing, Clustering, Bayesian Optimization, Lazy Learning, Genetic Programming, LSTMs.
  • Robotics: Stochastic Optimal Control, Movement Primitives, Reinforcement Learning, Imitation Learning, Morphological Computation, Quadruped Locomotion, Humanoid Postural Control, Grasping, Tactile Learning, Dynamic Control.
  • Human Motor Control & Science: Prosthesis Research & Rehabilitation, Motor Adaptation, Motor Skill Learning, Postural Control, Telepresence, Embodiment, Congruence in Teleoperation, Interactive Learning, Shared Control, Human Feedback.

Contact & Quick Links

Univ.-Prof. Dipl.-Ing. Dr.techn. Elmar Rueckert
Leiter des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1901 (Sekretariat CPS)
Email:   rueckert@unileoben.ac.at 
Web:  https://cps.unileoben.ac.at
Chat: WEBEX

Publcations

Journal Articles

Tanneberg, Daniel; Ploeger, Kai; Rueckert, Elmar; Peters, Jan

SKID RAW: Skill Discovery from Raw Trajectories Journal Article

IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, ISSN: 2377-3766, (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.).

Links | BibTeX

Jamsek, Marko; Kunavar, Tjasa; Bobek, Urban; Rueckert, Elmar; Babic, Jan

Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller Journal Article

IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, ISSN: 2377-3766, (© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.).

Links | BibTeX

Cansev, Mehmet Ege; Xue, Honghu; Rottmann, Nils; Bliek, Adna; Miller, Luke E; Rueckert, Elmar; Beckerle, Philipp

Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience Journal Article

Advanced Intelligent Systems, pp. 1–28, 2021.

Links | BibTeX

Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E

A novel Chlorophyll Fluorescence based approach for Mowing Area Classification Journal Article

IEEE Sensors Journal, 2020.

Links | BibTeX

Tanneberg, Daniel; Rueckert, Elmar; Peters, Jan

Evolutionary training and abstraction yields algorithmic generalization of neural computers Journal Article

Nature Machine Intelligence, pp. 1–11, 2020.

Links | BibTeX

Cartoni, E; Mannella, F; Santucci, V G; Triesch, J; Rueckert, E; Baldassarre, G

REAL-2019: Robot open-Ended Autonomous Learning competition Journal Article

Proceedings of Machine Learning Research, 123 , pp. 142-152, 2020, (NeurIPS 2019 Competition and Demonstration Track).

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Journal Article

Neural Networks – Elsevier, 109 , pp. 67-80, 2019, ISBN: 0893-6080, (Impact Factor of 7.197 (2017)).

Links | BibTeX

Sosic, Adrian; Zoubir, Abdelhak M; Rueckert, Elmar; Peters, Jan; Koeppl, Heinz

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal Article

Journal of Machine Learning Research (JMLR), 19 (69), pp. 1-45, 2018.

Links | BibTeX

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Probabilistic Movement Primitives under Unknown System Dynamics Journal Article

Advanced Robotics (ARJ), 32 (6), pp. 297-310, 2018.

Links | BibTeX

Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article

Nature Publishing Group: Scientific Reports, 6 (28455), 2016.

Links | BibTeX

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

Recurrent Spiking Networks Solve Planning Tasks Journal Article

Nature Publishing Group: Scientific Reports, 6 (21142), 2016.

Links | BibTeX

Rueckert, Elmar; d’Avella, Andrea

Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems Journal Article

Frontiers in Computational Neuroscience, 7 (138), 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard; Toussaint, Marc; Maass, Wolfgang

Learned graphical models for probabilistic planning provide a new class of movement primitives Journal Article

Frontiers in Computational Neuroscience, 6 (97), 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation Journal Article

Artificial Life, 19 (1), 2012.

Links | BibTeX

Inproceedings

Rottmann, N; Denz, R; Bruder, R; Rueckert, E

Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Inproceedings

European Conference on Mobile Robots (ECMR 2021), 2021.

Links | BibTeX

Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E

Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Inproceedings

IEEE SENSORS , pp. 1–4, 2020.

Links | BibTeX

Rottmann, N; Kunavar, T; Babič, J; Peters, J; Rueckert, E

Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings

International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020.

Links | BibTeX

Rottmann, N; Bruder, R; Xue, H; Schweikard, A; Rueckert, E

Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings

Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020.

Links | BibTeX

Tolga-Can Çallar, Elmar Rueckert ; Böttger, Sven

Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates Inproceedings

54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020.

Links | BibTeX

Xue, H; Boettger, S; Rottmann, N; Pandya, H; Bruder, R; Neumann, G; Schweikard, A; Rueckert, E

Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks Inproceedings

International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020.

Links | BibTeX

Stark, Svenja; Peters, Jan; Rueckert, Elmar

Experience Reuse with Probabilistic Movement Primitives Inproceedings

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019., 2019.

Links | BibTeX

Boettger, S; Callar, T C; Schweikard, A; Rueckert, E

Medical robotics simulation framework for application-specific optimal kinematics Inproceedings

Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019.

Links | BibTeX

Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E

Loop Closure Detection in Closed Environments Inproceedings

European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9.

Links | BibTeX

Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E

Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Inproceedings

Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019).

Links | BibTeX

Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim

Dynamic Control Strategies for Cable-Driven Master Slave Robots Inproceedings

Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019).

Links | BibTeX

Gondaliya, Kaushikkumar D; Peters, Jan; Rueckert, Elmar

Learning to Categorize Bug Reports with LSTM Networks Inproceedings

Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID)., pp. 6, XPS (Xpert Publishing Services), Nice, France, 2018, ISBN: 978-1-61208-671-2, ( October 14-18, 2018).

Links | BibTeX

Rueckert, Elmar; Nakatenus, Moritz; Tosatto, Samuele; Peters, Jan

Learning Inverse Dynamics Models in O(n) time with LSTM networks Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Efficient Online Adaptation with Stochastic Recurrent Neural Networks Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Stark, Svenja; Peters, Jan; Rueckert, Elmar

A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Thiem, Simon; Stark, Svenja; Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Simulation of the underactuated Sake Robotics Gripper in V-REP Inproceedings

Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017.

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Inproceedings

Proceedings of the Conference on Robot Learning (CoRL), 2017.

Links | BibTeX

Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar

Deep Spiking Networks for Model-based Planning in Humanoids Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Azad, Morteza; Ortenzi, Valerio; Lin, Hsiu-Chin; Rueckert, Elmar; Mistry, Michael

Model Estimation and Control of Complaint Contact Normal Force Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Kohlschuetter, Jan; Peters, Jan; Rueckert, Elmar

Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities Inproceedings

Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 2016.

Links | BibTeX

Modugno, Valerio; Neumann, Gerhard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena

Learning soft task priorities for control of redundant robots Inproceedings

Proceedings of the International Conference on Robotics and Automation (ICRA), 2016.

Links | BibTeX

Sharma, David; Tanneberg, Daniel; Grosse-Wentrup, Moritz; Peters, Jan; Rueckert, Elmar

Adaptive Training Strategies for BCIs Inproceedings

Cybathlon Symposium, 2016.

Links | BibTeX

Weber, Paul; Rueckert, Elmar; Calandra, Roberto; Peters, Jan; Beckerle, Philipp

A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction Inproceedings

Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016.

Links | BibTeX

Calandra, Roberto; Ivaldi, Serena; Deisenroth, Marc; Rueckert, Elmar; Peters, Jan

Learning Inverse Dynamics Models with Contacts Inproceedings

Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

Links | BibTeX

Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard

Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings

Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

Links | BibTeX

Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard

Model-Free Probabilistic Movement Primitives for Physical Interaction Inproceedings

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015.

Links | BibTeX

Rueckert, Elmar; Lioutikov, Rudolf; Calandra, Roberto; Schmidt, Marius; Beckerle, Philipp; Peters, Jan

Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations Inproceedings

ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015.

Links | BibTeX

Rueckert, Elmar; Mindt, Max; Peters, Jan; Neumann, Gerhard

Robust Policy Updates for Stochastic Optimal Control Inproceedings

Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014.

Links | BibTeX

Rueckert, Elmar; d’Avella, Andrea

Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems Inproceedings

Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp. 27–28, 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

A study of Morphological Computation by using Probabilistic Inference for Motor Planning Inproceedings

Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp. 51–53, 2011.

Links | BibTeX

Masters Theses

Rueckert, Elmar

Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis

Technical University Graz, 2010.

Links | BibTeX

PhD Theses

Rueckert, Elmar

Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis

Technical University Graz, 2014.

Links | BibTeX

Track Record

News

July 20, 2021

Conference Paper accepted at ECMR 2021

The paper by Nils Rottmann, Robin Denz, Ralf Bruder and Elmar Rueckert on “Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems” was accepted at the European Conference on Mobile Robotics (ECMR).

May 7, 2021

Conference Paper accepted at HUMANOIDS 2021

The paper by Marko Jamsek, Tjasa Kunavar, Urban Bobek, Elmar Rueckert and Jan Babic on Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller was accepted at the IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids).

March 11, 2021

Journal paper of the DFG TRAIN project team accepted

The paper on “Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience” by Mehmet Ege Cansev, Honghu Xue, Nils Rottmann, Adna Bliek, Luke E. Miller, Elmar Rueckert and Philipp Beckerle was accepted for publication at the Advanced Intelligent Systems.

March 11, 2021

Journal Paper accepted at IEEE RA-L

The paper on “SKID RAW: Skill Discovery from Raw Trajectories“, by Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters was accepted for publication at IEEE Robotics and Automation Letters(RA-L).

March 11, 2021

Journal Paper accepted at IEEE RA-L

The paper “Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller” by Marko Jamsek and Tjasa Kunavar and Urban Bobek and Elmar Rueckert and Jan Babic was accepted for publication at IEEE Robotics and Automation Letters (RA-L).

March 3, 2021

1st of March 2021 Starting as Chair of the Cyber-Physical-Systems Lab at Leoben

With March 1st, 2021, Prof. Rueckert chairs the Cyber-Physic al-Systems Institute at the Montanuniversität in Leoben, Austria. This new Institute will focus on robotics and machine learning research and will contribute to the data science master program.

December 3, 2020

Successful Graduation of Daniel Tanneberg

Congratulations to Daniel Tanneberg for completing his PhD. He is the first graduate of Prof. Elmar Rueckert’s group.

October 20, 2020

Journal Paper accepted at IEEE Sensors Journal

Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert A novel Chlorophyll Fluorescence based approach for Mowing Area Classification accepted (Oct, 12th 2020) at IEEE Sensors Journal with an Impact Factor of 3 (2019).

August 28, 2020

Conference Paper Accepted at IEEE Sensors

The paper by  Nils Rottmann, Ralf Burder, Achim Schweikard und Elmar Rueckert on Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors was accepted for publication at the IEEE SENSORS 2020 Conference, to be held from October 25-28, 2020.

July 16, 2020

Workshop accepted at IROS 2020

Our workshop on „New Horizons for Robot Learning“ was accepted at the  International Conference on Intelligent Robots and Systems (IROS’ 2020). See https://iros.ai-lab.science

July 16, 2020

Conference Paper accepted at BMT2020

The paper by Tolga-Can Çallar, Elmar Rueckert and Sven Böttger on “Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates” was accepted for publication in the 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020). .

July 9, 2020

Conference Paper accepted at IROS 2020

The paper by Nils Rottmann, Tjaša Kunavar, Jan Babič, Jan Peters and Elmar Rueckert on “Learning Hierarchical Acquisition Functions for Bayesian Optimization” was accepted for publication at the International Conference on Intelligent Robots and Systems (IROS’ 2020).

June 23, 2020

Proceedings of Machine Learning Research Paper accepted

E. Cartoni, F. Mannella, V.G. Santucci, J. Triesch, E. Rueckert, G. Baldassarre. REAL-2019: Robot open-Ended Autonomous Learning competition. Proceedings of Machine Learning Research 123:142-152, 2020. NeurIPS 2019 Competition and Demonstration Track

February 3, 2020

Conference Paper accepted at ASPAI 2020

The paper by Honghu Xue, Sven Boettger, Nils Rottmann, Harit Pandya, Ralf Bruder, Gerhard Neumann, Achim Schweikard and Elmar Rueckert on “Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks” was accepted for publication at the 2nd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020).

December 3, 2019

Successful grant: DFG project with > 650kEURO granted

Together with Prof. Philipp Beckerle from the TU Dortmund, we got our research project on ‘Active transfer learning with neural networks through human-robot interactions’ granted.

August 26, 2019

Winner of the ’German AI-Young Research Price 2019’

Prof. Rueckert won the ’German AI-Young Researcher Price 2019’  (germ. deutscher KI-Nachwuchspreis 2019) by Bilanz & McKinsey – KI-Denker der Zukunft. The awards ceremony was on Sept. 26th, 2019. The main AI price was given to Prof. Kristian Kersting from the TU-Darmstadt. The german company DeepL received the award for applied AI.

July 26, 2019

Advanced Robotics Best Paper Award

for the paper: Probabilistic Movement Primitives under Unknown System Dynamics, by Paraschos, Alexandros and Rueckert, Elmar and Peters, Jan and Neumann, Gerhard. Advanced Robotics (ARJ), 32 (6), pp. 297-310, 2018.

June 22, 2019

Conference Paper accepted at ECMR 2019

The paper by Nils Rottmann, Ralf Bruder, Achim Schweikard and Elmar Rueckert on “Loop Closure Detection in Closed Environments” was accepted for publication at the 2019 European Conference on Mobile Robots (ECMR).

June 20, 2019

Conference Paper Accepted at IROS 2019

The paper by Svenja Stark, Jan Peters and Elmar Rueckert on “Experience Reuse with Probabilistic Movement Primitives” was accepted for publication in the Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019).

June 7, 2019

Successful grant: Autonome Elektrofahrzeuge als urbane Lieferanten

Das Projekt Autonome Elektrofahrzeuge als urbane Lieferanten wird im Rahmen des Programms „Our Common Future“ von der Robert Bosch Stiftung gefördert. Projektstart ist der 01.07.2019 bis 30.10.2021 More at: https://future.ai-lab.science

April 17, 2019

Gründungssitzung Grundlagen von KI Systemen

Fachausschusses FA1.60 zu Grundlagen lernender intelligenter Systeme, Gründungsmitglieder: Barbara Hammer (Universität Bielefeld), Elmar Rückert (gewählter Vorsitzender), Georg Schildbach (Universität zu Lübeck), Gerhard Neumann (Universität Tübingen), Heinz Koeppl (Technische Universität Darmstadt), Jan Peters (Technische Universität Darmstadt), Justus Piater (Universität Innsbruck), Kristian Kersting (Technische Universität Darmstadt), Marc Toussaint (Universität Stuttgart), Micheal Ginger (Honda Research, Offenbach), Philipp Beckerle […]

January 5, 2019

Best Paper Award

for the paper: Learning to Categorize Bug Reports with LSTM Networks, by Gondaliya, Kaushikkumar D; Peters, Jan; Rueckert, Elmar.  In Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID)., pp. 6, XPS (Xpert Publishing Services), Nice, France, 2018, ISBN: 978-1-61208-671-2, ( October 14-18, 2018).

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 Signal Processing (BIOSIGNALS).

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 of 7.197 (2017).

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 company, Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID).

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 Stochastic Recurrent Neural Networks. Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries.

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 actuators and various types of sensors (e.g., depth and vision cameras, tactile fingertips, full-body skin, proprioception) have reached the perceptuomotor complexity faced in human motor […]

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 central role, the functional principles underlying planning are largely unexplored. In this talk, I present a computational model for planning that is derived from theoretical […]

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.

March 1, 2014

Postdoctoral fellow at IAS, Darmstadt

Elmar Rueckert joined the Autonomous Systems Labs of Prof. Jan Peters as Post-Doc in March 2014.

February 4, 2014

Ph.D. Defense – Summa Cum Laude (with honors).

At the Technical University Graz, Austria with Prof. Wolfgang Maass.

June 1, 2013

Two Journal Papers Accepted at Frontiers in Computational Neurosciene

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 probabilistic planning provide a new class of movement primitives

January 28, 2010

M.Sc. defense – Summa Cum Laude (with honors).

At the technical University Graz with Prof. Horst Bischof.