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Prof. Elmar Rueckert (Chair)

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

CV of Prof. Elmar Rueckert
DBLP
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GitHub
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LinkedIn
ORCID
Research Gate

Publcations

Journal Articles

Kunavar, Tjasa; Jamšek, Marko; Avila-Mireles, Edwin Johnatan; Rueckert, Elmar; Peternel, Luka; Babič., Jan

The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task Journal Article

In: Sensors (MDPI), vol. 24, no. 4, pp. 1–17, 2024.

Links | BibTeX

Nwankwo, Linus; Fritze, Clemens; Bartsch, Konrad; Rueckert, Elmar

ROMR: A ROS-based Open-source Mobile Robot Journal Article

In: HardwareX, vol. 15, pp. 1–29, 2023.

Abstract | Links | BibTeX

Herzog, Rebecca; Berger, Till M; Pauly, Martje Gesine; Xue, Honghu; Rueckert, Elmar; Munchau, Alexander; B"aumer, Tobias; Weissbach, Anne

Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques Journal Article

In: Frontiers in Neuroscience, 2022.

Links | BibTeX

Rottmann, Nils; Studt, Nico; Ernst, Floris; Rueckert, Elmar

ROS-Mobile: An Android™ application for the Robot Operating System Journal Article

In: Arxiv, 2022.

Links | BibTeX

Xue, Honghu; Hein, Benedikt; Bakr, Mohamed; Schildbach, Georg; Abel, Bengt; Rueckert, Elmar

Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics Journal Article

In: Applied Sciences (MDPI), Special Issue on Intelligent Robotics, 2022, (Supplement: https://cloud.cps.unileoben.ac.at/index.php/s/Sj68rQewnkf4ppZ).

Abstract | Links | BibTeX

Xue, Honghu; Herzog, Rebecca; Berger, Till M.; Bäumer, Tobias; Weissbach, Anne; Rueckert, Elmar

Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation Journal Article

In: Frontiers in Robotics and AI , vol. 8, 2021, ISSN: 2296-9144.

Abstract | Links | BibTeX

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

SKID RAW: Skill Discovery from Raw Trajectories Journal Article

In: 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

In: 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

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

Links | BibTeX

Kyrarini, Maria; Lygerakis, Fotios; Rajavenkatanarayanan, Akilesh; Sevastopoulos, Christos; Nambiappan, Harish Ram; Chaitanya, Kodur Krishna; Babu, Ashwin Ramesh; Mathew, Joanne; Makedon, Fillia

A Survey of Robots in Healthcare Journal Article

In: Technologies, vol. 9, iss. 8, 2021.

Links | BibTeX

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

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

In: IEEE Sensors Journal, 2020.

Links | BibTeX

Tanneberg, Daniel; Rueckert, Elmar; Peters, Jan

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

In: 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

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

Links | BibTeX

Diakoloukas, Vassilios; Lygerakis, Fotios; Lagoudakis, Michail G; Kotti, Margarita

Variational Denoising Autoencoders and Least-Squares Policy Iteration for Statistical Dialogue Manager Journal Article

In: IEEE Signal Processing Letters , vol. 27, pp. 960-964, 2020.

Links | BibTeX

Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar

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

In: Neural Networks - Elsevier, vol. 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

In: Journal of Machine Learning Research (JMLR), vol. 19, no. 69, pp. 1-45, 2018.

Links | BibTeX

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

Probabilistic Movement Primitives under Unknown System Dynamics Journal Article

In: Advanced Robotics (ARJ), vol. 32, no. 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

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 28455, 2016.

Links | BibTeX

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

Recurrent Spiking Networks Solve Planning Tasks Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 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

In: Frontiers in Computational Neuroscience, vol. 7, no. 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

In: Frontiers in Computational Neuroscience, vol. 6, no. 97, 2013.

Links | BibTeX

Rueckert, Elmar; Neumann, Gerhard

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

In: Artificial Life, vol. 19, no. 1, 2012.

Links | BibTeX

Conferences

Lygerakis, Fotios; Dave, Vedant; Rueckert, Elmar

M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation Conference Forthcoming

arXiv:2401.17032 [cs.RO], Forthcoming.

Links | BibTeX

Lygerakis, Fotios; Dagioglou, Maria; Karkaletsis, Vangelis

Accelerating Human-Agent Collaborative Reinforcement Learning Conference

In Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA '21), Association for Computing Machinery, New York, NY, USA, 90–92, 2021.

Links | BibTeX

Banerjee, Debapriya; Lygerakis, Fotios; Makedon, Fillia

Sequential Late Fusion Technique for Multi-modal Sentiment Analysis Conference

In Proceedings of the 14th PErvasive Technologies Related to Assistive Environments Conference (PETRA '21), Association for Computing Machinery, New York, NY, USA, 264–265. , 2021.

Links | BibTeX

Lygerakis, Fotios; Tsitos, Athanasios C; Dagioglou, Maria; Makedon, Fillia; Karkaletsis, Vangelis

Evaluation of 3D markerless pose estimation accuracy using openpose and depth information from a single RGB-D camera Conference

In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '20), Article 75, 1–6 Association for Computing Machinery, New York, NY, USA, 2020.

Links | BibTeX

Lygerakis, Fotios; Diakoloulas, Vassilios; Lagoudakis, Michail; Kotti, Margarita

Robust Belief State Space Representation for Statistical Dialogue Managers Using Deep Autoencoders Conference

2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019.

Links | BibTeX

Proceedings Articles

Dave*, Vedant; Lygerakis*, Fotios; Rueckert, Elmar

Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training Proceedings Article

In: IEEE International Conference on Robotics and Automation (ICRA 2024)., 2024, (* equal contribution).

Links | BibTeX

Nwankwo, Linus; Rueckert, Elmar

The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language Proceedings Article

In: ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24 Companion)., 2024, (Published as late breaking results. Supplementary video: https://cloud.cps.unileoben.ac.at/index.php/s/fRE9XMosWDtJ339 ).

Links | BibTeX

Lygerakis, Fotios; Rueckert, Elmar

CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse Proceedings Article

In: Asian Conference of Artificial Intelligence Technology (ACAIT)., IEEE, 2023.

Links | BibTeX

Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Ngoc Thinh

Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot Proceedings Article

In: International Conference on System Theory, Control and Computing (ICSTCC), 2023, (October 11-13, 2023, Timisoara, Romania.).

Links | BibTeX

Nwankwo, Linus; Rueckert, Elmar

Understanding why SLAM algorithms fail in modern indoor environments Proceedings Article

In: International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). , pp. 186 - 194, Cham: Springer Nature Switzerland., 2023.

Abstract | Links | BibTeX

Keshavarz, Sahar; Vita, Petr; Rueckert, Elmar; Ortner, Ronald; Thonhauser, Gerhard

A Reinforcement Learning Approach for Real-Time Autonomous Decision-Making in Well Construction Proceedings Article

In: Society of Petroleum Engineers - SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry, AIS 2023, Society of Petroleum Engineers., 2023, ISBN: 9781613999882.

Links | BibTeX

Xue, Honghu; Song, Rui; Petzold, Julian; Hein, Benedikt; Hamann, Heiko; Rueckert, Elmar

End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments Proceedings Article

In: International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Dave, Vedant; Rueckert, Elmar

Predicting full-arm grasping motions from anticipated tactile responses Proceedings Article

In: International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Leonel, Rozo*; Vedant, Dave*

Orientation Probabilistic Movement Primitives on Riemannian Manifolds Proceedings Article

In: Conference on Robot Learning (CoRL), pp. 11, 2022, (* equal contribution).

Abstract | Links | BibTeX

Denz, R.; Demirci, R.; Cansev, E.; Bliek, A.; Beckerle, P.; Rueckert, E.; Rottmann, N.

A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning Proceedings Article

In: International Conference on Advanced Robotics , pp. 7, 2021.

Links | BibTeX

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

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

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

Links | BibTeX

Akbulut, M Tuluhan; Oztop, Erhan; Seker, M Yunus; Xue, Honghu; Tekden, Ahmet E; Ugur, Emre

ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing Proceedings Article

In: 2020.

Abstract | Links | BibTeX

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

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

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

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).

Links | BibTeX

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

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

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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 Proceedings Article

In: 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

Proceedings

Dave, Vedant; Lygerakis, Fotios; Rueckert, Elmar

Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training Proceedings Forthcoming

Forthcoming, (Website: https://sites.google.com/view/mvitac/home).

Abstract | Links | BibTeX

Workshops

Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Thinh

Deep Reinforcement Learning for Autonomous Navigation in Intralogistics Workshop

2023, (European Control Conference (ECC) Workshop, Extended Abstract.).

Abstract | Links | BibTeX

Dave, Vedant; Rueckert, Elmar

Can we infer the full-arm manipulation skills from tactile targets? Workshop

International Conference on Humanoid Robots (Humanoids 2022), 2022.

Abstract | Links | BibTeX

Track Record

News

February 27, 2024

Journal Paper accepted at Sensors (MDPI)

Journal Paper accepted at Sensors (MDPI)

The paper by Kunavar, T., Jamšek, M., Avila-Mireles, E. J., Rueckert, E., Peternel, L., and Babič J. on “The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task” was published at Sensors (MDPI) in Feb. 2024.

February 2, 2024

Conference Paper Accepted at ICRA 2024

Conference Paper Accepted at ICRA 2024

The paper on ‘Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training‘ by Vedant Dave*, Fotios Lygerakis* and Elmar Rueckert was accepted for publication in the IEEE International Conference on Robotics and Automation (ICRA 2024). *Both authors contributed equally.

January 16, 2024

Conference Paper accepted at HRI 2024

Conference Paper accepted at HRI 2024

The paper on ‘The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language‘ by Linus Nwankwo and Elmar Rueckert was accepted for publication the the International Conference on Human-Robot Interaction (HRI ’24 Companion) as late breaking results paper.

November 10, 2023

FFG Project Grant – NNATT

FFG Project Grant – NNATT

Our joint proposal on “Sustainable use of excavated materials from civil engineering and tunnel construction using sensor-based technologies” was granted by the  Austrian Research Promotion Agency (FFG). The project starts in 2024. Two Ph.Ds will be hired and jointly supervised with the chair of subsurface engineering and the chair of waste recycling technology and waste […]

August 25, 2023

Conference Paper accepted at ACAIT 2023

Conference Paper accepted at ACAIT 2023

The paper on ‘CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse‘ by Fotios LYgerakis and Elmar Rueckert was accepted for publication at the Asian Conference of Artificial Intelligence Technology (ACAIT), IEEE.

July 10, 2023

Conference Paper accepted at ICSTCC 2023

Conference Paper accepted at ICSTCC 2023

The paper on Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot by Yadav, Harsh; Xue, Honghu; Rudall, Yan; Bakr, Mohamed; Hein, Benedikt; Rueckert, Elmar; Nguyen, Ngoc Thinhwas accepted for publication at the International Conference on System Theory, Control and Computing (ICSTCC), October 11-13, 2023. Timisoara, Romania.

June 19, 2023

Best Student Paper Award – Linus Nwankwo

Best Student Paper Award – Linus Nwankwo

Congratulations to Linus Nwankwo for winning the best student paper award at the RAAD2023 conference for his paper on why SLAM algorithms fail in modern indoor environments, https://cloud.cps.unileoben.ac.at/index.php/s/KdZ2E2np5QEnYfL  

April 17, 2023

Journal Paper Accepted at Hardware X

Journal Paper Accepted at Hardware X

The paper by Linus Nwankwo, Clemens Fritze, Konrad Bartsch, and Elmar Rueckert on “ROMR: A ROS-based Open-source Mobile Robot” was accepted for publication in the journal Hardware X.

March 13, 2023

Conference Paper accepted at RAAD 2023

Conference Paper accepted at RAAD 2023

The paper on Understanding why SLAM algorithms fail in modern indoor environments by Linus Nwankwo and Rueckert Elmar was accepted for publication at the International Conference on Robotics in Alpe-Adria-Danube Region (RAAD 2023), 2023.

January 13, 2023

FFG Project Grant – KIRAMET

FFG Project Grant – KIRAMET

Our joint proposal on “AI for recycling 2022” (germ. Künstliche Intelligenz für Recycling 2022) was granted by the  Austrian Research Promotion Agency (FFG). The project starts in March/April 2023 and CPS will hire Ph.D. for three years to work on this project.

September 29, 2022

Conference Paper accepted at HUMANOIDS 2022

Conference Paper accepted at HUMANOIDS 2022

The paper on End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments by Honghu Xue, Rui Song, Julian Petzold, Benedikt Hein, Heiko Hamann and Rueckert Elmar was accepted for publication at the International Conference on Humanoid Robots (Humanoids 2022), 2022.

September 29, 2022

Conference Paper accepted at HUMANOIDS 2022

Conference Paper accepted at HUMANOIDS 2022

The paper on Predicting full-arm grasping motions from anticipated tactile responsess by Dave Vedant and Rueckert Elmar was accepted for publication at the International Conference on Humanoid Robots (Humanoids 2022), 2022.

September 20, 2022

Journal Paper Accepted at Frontiers in Neuroscience

Journal Paper Accepted at Frontiers in Neuroscience

The paper by Rebecca Herzog and Till M Berger and Martje Gesine Pauly and Honghu Xue and Elmar Rueckert and Alexander Munchau and Tobias Bäumer and Anne Weissbach on “Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques” was published in the journal Frontiers in Neuroscience.

March 17, 2022

Journal Paper Accepted at Applied Sciences MDPI

Journal Paper Accepted at Applied Sciences MDPI

The paper by Honghu Xue and Benedikt Hein and Mohamed Bakr and Georg Schildbach and Bengt Abel and Elmar Rueckert on “Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics” was accepted for publication at Applied Sciences MDPI.

March 1, 2022

Successful grant

Successful grant

Our grant application for building an AI Robot Lab was funded. We will set up an industrial robot learning lab with two universal robotics UR3e arms, two FANUC CRX10iA robot arms a professional lathe from ELMAG a  drill milling machine from ELMAG and roller conveyor belt. Find out more at https://cps.unileoben.ac.at/2022/03/08/ai-robot-lab/.

October 6, 2021

Conference Paper accepted at ICAR 2021

The paper on A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning by Robin Denz*, Rabia Demirci, Mehmet Ege Cansev, Adna Bliek, Beckerle Beckerle, Elmar Rueckert and Nils Rottmann was accepted at the 20th International Conference on Advanced Robotics (ICAR), December 6-10, 2021, Ljubljana, Slovenia.

September 14, 2021

Journal Paper accepted at Frontiers in Robotics and AI

Xue Honghu, Herzog Rebecca, Berger Till M., Bäumer Tobias, Weissbach Anne and Rueckert Elmar published the article on “Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation” at the journal Frontiers in Robotics and AI in September 2021.

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.