Elmar Rueckert's Curriculum Vitae

Personal Data

Since 03/2014Postdoctoral fellow at Intelligent Autonous Systems Group
 Team Leader of EU-Project CoDyCo
 Technische Universität Darmstadt, Germany
 Supervisor: Prof. Dr. J. Peters.
  

Educational Background

2010-2014Ph.D. (Dr.techn.) in Computer Science (with honors)
 "On Biologically inspired motor skill learning in robotics through probabilistic inference".
 University of Technology, Graz, Austria.
 Supervisors: Prof. Dr. Wolfgang Maass.
2003-2010Bachelor and Master in Telematics with the specialization on artificial intelligence and computer vision (with honors).
 University of Technology, Graz, Austria.
 Master's thesis: "Simultaneous localization and mapping for mobile robots with recent sensor technologies" (2010) .
 Supervisors: Prof. Dr. Horst Bischof.
  

Internships

2012Research internship under supervision of Prof. Benjamin Schrauwen, Reservoir Lab, Gent, Belgium.
 During this internship I applied probabilistic planning and model learning methods to a cart-pole robot.
2012Research internship under supervision of Prof. Andrea d'Avella, Laboratory of Neuromotor Physiology, Santa Lucia Foundation, Rome, Italy.
 During this internship I developed together with Prof. Andrea d'Avella a novel movement representation that is based on parametrized muscle synergies in dynamical systems. I presented this work at the highly competetive Neural Control of Movements Conference in Puerto Rico, USA.
2011Machine Learning Summer School, Bordeaux, France.
 In this two weeks summer school advanced machine learning and robot learning techniques were presented by leading scientists in that areas.

Participation in EU-Projects

EU-FP7 Project TACMAN, Co-Supervisor of Daniel Tanneberg2014-2017
EU-FP7 Project CoDyCo, Team Leader for TU-Darmstadt2014-2017
EU-FP7 Project AMARSi, Leader of WP5-Learning on behalf of Prof. Dr. Wolfgang Maass2010-2014

Workshops and Summer School Talks

  • [2014, Invited Talk] TEDUSAR Summer School. Title: An introduction to robot learning and probabilistic movement planning.
  • [2011, Organizer of a two-days WS] AMARSI EU-project. Title: Hands-on Probabilistic Inference for Motor Control. Co-organized with Gerhard Neumann

Publications

Journal Papers
Tanneberg, D.; Ploeger, K.; Rueckert, E.; Peters, J. (2021). SKID RAW: Skill Discovery from Raw Trajectories, IEEE Robotics and Automation Letters (RA-L).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Rueckert, E.; Peters, J. (2020). Evolutionary training and abstraction yields algorithmic generalization of neural computers, Nature Machine Intelligence, 2, 12, pp.753-763.   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Peters, J.; Rueckert, E. (2019). Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks, Neural Networks, 109, pp.67-80.   Download Article [PDF]   BibTeX Reference [BibTex]

Paraschos, A.; Rueckert, E.; Peters, J.; Neumann, G. (2018). Probabilistic Movement Primitives under Unknown System Dynamics, Advanced Robotics (ARJ), 32, 6, pp.297-310.   Download Article [PDF]   BibTeX Reference [BibTex]

Sosic, A.; Rueckert, E.; Peters, J.; Zoubir, A.M.; Koeppl, H (2018). Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling, Journal of Machine Learning Research (JMLR), 19, 69, pp.1--45.   BibTeX Reference [BibTex]

Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D; Peters, J. (2016). Recurrent Spiking Networks Solve Planning Tasks, Nature PG: Scientific Reports, 6, 21142, Nature Publishing Group.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Camernik, J.; Peters, J.; Babic, J. (2016). Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control, Nature PG: Scientific Reports, 6, 28455.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.A.; Neumann, G.; Toussaint, M.; Maass, W. (2013). Learned graphical models for probabilistic planning provide a new class of movement primitives, Frontiers in Computational Neuroscience, 6, 97.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.A.; d'Avella, A. (2013). Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems, Frontiers in Computational Neuroscience, 7, 138.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.A.; Neumann, G. (2012). Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation, Artificial Life.   Download Article [PDF]   BibTeX Reference [BibTex]
 
Conference and Workshop Papers
Stark, S.; Peters, J.; Rueckert, E. (2019). Experience Reuse with Probabilistic Movement Primitives, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Gondaliya, K.D.; Peters, J.; Rueckert, E. (2018). Learning to Categorize Bug Reports with LSTM Networks, Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle.   Download Article [PDF]   BibTeX Reference [BibTex]

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).   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Nakatenus, M.; Tosatto, S.; Peters, J. (2017). Learning Inverse Dynamics Models in O(n) time with LSTM networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Efficient Online Adaptation with Stochastic Recurrent Neural Networks, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Stark, S.; Peters, J.; Rueckert, E. (2017). A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Thiem, S.; Stark, S.; Tanneberg, D.; Peters, J.; Rueckert, E. (2017). Simulation of the underactuated Sake Robotics Gripper in V-REP, Workshop at the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Kohlschuetter, J.; Peters, J.; Rueckert, E. (2016). Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities, Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON).   Download Article [PDF]   BibTeX Reference [BibTex]

Modugno, V.; Neumann, G.; Rueckert, E.; Oriolo, G.; Peters, J.; Ivaldi, S. (2016). Learning soft task priorities for control of redundant robots, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Sharma, D.; Tanneberg, D.; Grosse-Wentrup, M.; Peters, J.; Rueckert, E. (2016). Adaptive Training Strategies for BCIs, Cybathlon Symposium.   Download Article [PDF]   BibTeX Reference [BibTex]

Weber, P.; Rueckert, E.; Calandra, R.; Peters, J.; Beckerle, P. (2016). A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction, Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).   Download Article [PDF]   BibTeX Reference [BibTex]

Tanneberg, D.; Paraschos, A.; Peters, J.; Rueckert, E. (2016). Deep Spiking Networks for Model-based Planning in Humanoids, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Azad, M.; Ortenzi, V.; Lin, H., C.; Rueckert, E.; Mistry, M. (2016). Model Estimation and Control of Complaint Contact Normal Force, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Calandra, R.; Ivaldi, S.; Deisenroth, M.;Rueckert, E.; Peters, J. (2015). Learning Inverse Dynamics Models with Contacts, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Mundo, J.; Paraschos, A.; Peters, J.; Neumann, G. (2015). Extracting Low-Dimensional Control Variables for Movement Primitives, Proceedings of the International Conference on Robotics and Automation (ICRA).   Download Article [PDF]   BibTeX Reference [BibTex]

Paraschos, A.; Rueckert, E.; Peters, J; Neumann, G. (2015). Model-Free Probabilistic Movement Primitives for Physical Interaction, Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS).   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Lioutikov, R.; Calandra, R.; Schmidt, M.; Beckerle, P.; Peters, J. (2015). Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations, ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.; Mindt, M.; Peters, J.; Neumann, G. (2014). Robust Policy Updates for Stochastic Optimal Control, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.A.; d'Avella, A. (2013). Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems, Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp.27--28.   Download Article [PDF]   BibTeX Reference [BibTex]

Rueckert, E.A.; Neumann, G. (2011). A study of Morphological Computation by using Probabilistic Inference for Motor Planning, Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp.51--53.   Download Article [PDF]   BibTeX Reference [BibTex]

Manuscripts under review

Nov. 2015Postural control predicts volitional motor control
 Rueckert, E.; Camernik, J.; Peters, J.; Babic, J.
 cover letter (paper available upon request)
Sep. 2015Recurrent Spiking Networks Solve Planning Tasks
 Rueckert, E.; Kappel, D.; Tanneberg, D.; Pecevski, D; Peters, J.
 cover letter (paper available upon request)
  

Reviewing Experience

Year(s)TypeVenue
2015ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2015ConferenceRobotics: Science and Systems (RSS)
2014ConferenceIEEE/RSJ International Conference on Humanoid Robots (HUMANOIDS)
2013ConferenceInternational Joint Conference on Artificial Intelligence (IJCAI)
2015, 2014, 2011ConferenceInternational Conference on Robotics and Automation (ICRA)
2015JournalInternational Journal of Robotics Research (IJRR)
2015JournalScientific Reporting, Nature Publishing Group
2015, 2014JournalAutonomous Robots
2013, 2012JournalFrontiers in Computational Neuroscience
2012JournalJournal of Neurophysiology
2012JournalArtificial Life Journal

Teaching Experience

YearUniversityLectureRoleLinkEvaluation
2015TU-DarmstadtMachine Learning - Statistical Approaches 1Teaching assistantcourse page 
2013-2014TU-GrazDatenstrukturen und AlgorithmenLecturercourse page 
2012-2013TU-GrazDatenstrukturen und AlgorithmenLecturercourse page 
2012-2013TU-GrazMachine Learning BTeaching assistantcourse pageevaluation report
2011-2012TU-GrazMachine Learning ATeaching assistantcourse page 

Student Supervision

YearUniversityStudentTypeTopicDocument
2015 (ongoing)TU-DarmstadtDaniel TannebergPh.D.Machine Learning for Tactile Manipulation 
2015TU-DarmstadtJan KohlschuetterM.Sc.Learning Probabilistic Classifiers from Electromyography Data for Predicting Knee Abnormalities 
2015TU-DarmstadtSvenja StarkM.Sc.Learning Probabilistic Models for Locomotion 
2015TU-DarmstadtDaniel TannebergM.Sc.Spiking Neural Networks Solve Robot Planning Problemspdf Δ
2014TU-DarmstadtMax MindtM.Sc.Probabilistic Inference for Movement Planning in Humanoidspdf Δ
2014TU-DarmstadtJan MundoM.Sc.Structure Learning for Movement Primitivespdf Δ
2013TU-GrazOliver PrevenhueberM.Sc.Monte Carlo Sampling Methods for Motor Control of Constraint High-dimensional Systemsavailable on request
2013TU-GrazOthmar GsengerM.Sc.Probabilistic Models for Learning the Dynamics Model of Robotsavailable on request
2013TU-GrazGerhard KniewasserM.Sc. (Project)Reinforcement Learning with Dynamic Movement Primitives - DMPsavailable on request
2012TU-GrazOliver PrevenhueberM.Sc. (Project)Gibbs Sampling Methods for Motor Control Problems with Hard Constraintsavailable on request
2012TU-GrazTim GeneweinM.Sc.Structure Learning for Motor Controlavailable on request
2011TU-GrazThomas WiesnerB.Sc.Ein Vergleich von Lernalgorithmen für Parametersuche im hochdimensionalen Raumavailable on request

Outreach activities

Sep. 2015Kinderuni Darmstadt 
 Interactive robot demos with the Nao, the iCub and the Darias robots 
Mar. 2015KID Science Radioclub 
 Lab tour and life demonstrations 
 Report of the kids 
   

  

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