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The neural learning for robotics research (NLR) laboratory is lead by Prof. Elmar Rueckert and located at the Institute for Robotics and Cognitive Systems at University of Lübeck and affiliated with the Technical University Darmstadt.

The group’s research topics are autonomous systems, machine and deep learning, brain-computer interfaces and simulations and computational models.

Short bio:

Elmar Rueckert is professor of Robotics and Autonomous Systems at the University of Luebeck and head of the research group “Neural Learning Methods for Robotics“. In 2014, he received his PhD in computer science at the Graz University of Technology. His dissertation on “biologically inspired motor learning methods for robots using probabilistic inference” was awarded summa cum laude. Thereafter he worked as a doctoral scientist at the Institute for Intelligent Autonomous Systems at the Technical University of Darmstadt. In 2016 he became the group leader of the research group “Neuronal Learning Methods for Robotics” and co-superviser of two PhD students. At the same time, he became coordinator of the associated EU project on cognitive learning methods in robotics.

At the beginning of 2018, Elmar Rueckert was appointed to the University of Luebeck, where his research interests include learning autonomous systems. Elmar Rueckert gives bachelor and master lectures in humanoid robotics, probabilistic robotics and machine learning. His research has contributed significantly to the understanding of stochastic and neuronal control and learning methods in robotics. His work on model predictive control with neuronal networks has been crucial for the realization of recent breakthroughs in real-time control strategies of humanoid robots with event based neuronal networks.. CV of Prof. Elmar Rueckert, E-Mail.

News

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..Read More

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..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 actuators and various types of sensors (e.g., depth and vision cameras, tactile fingertips, full-body skin, proprioception) have reached the perceptuomotor..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 central role, the functional principles underlying planning are largely unexplored. In this talk, I present a computational model for planning..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.

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.