Univ.-Prof. Dr. Elmar Rueckert
Chair of Cyber-Physical-Systems

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 |
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. @article{Herzog2022, |
Rottmann, Nils; Studt, Nico; Ernst, Floris; Rueckert, Elmar ROS-Mobile: An Android™ application for the Robot Operating System Journal Article In: Arxiv, 2022. @article{Rottmann2022, |
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). @article{Xue2022, We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. The automatic guided vehicle is equipped with LiDAR and frontal RGB sensors and learns to reach underneath the target dolly. The challenges reside in the sparseness of positive samples for learning, multi-modal sensor perception with partial observability, the demand for accurate steering maneuvers together with long training cycles. To address these points, we proposed NavACL-Q as an automatic curriculum learning together with distributed soft actor-critic. The performance of the learning algorithm is evaluated exhaustively in a different warehouse environment to check both robustness and generalizability of the learned policy. Results in NVIDIA Isaac Sim demonstrates that our trained agent significantly outperforms the map-based navigation pipeline provided by NVIDIA Isaac Sim in terms of higher agent-goal distances and relative orientations. The ablation studies also confirmed that NavACL-Q greatly facilitates the whole learning process and a pre-trained feature extractor manifestly boosts the training speed. |
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. @article{Rueckert2021, In medical tasks such as human motion analysis, computer-aided auxiliary systems have become preferred choice for human experts for its high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives(ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with10participants. The results validate ProMPs as a robust and effective feature extractor for human motions. |
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.). @article{Tanneberg2021, |
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.). @article{Jamsek2021, |
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. @article{Cansev2021, |
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. @article{Rottmann2020d, |
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. @article{Tanneberg2020, |
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). @article{Cartoni2020, |
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)). @article{Tanneberg2019, |
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. @article{Sosic2018, |
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. @article{Paraschos2018, |
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. @article{Rueckert2016b, |
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. @article{Rueckert2016a, |
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. @article{Rueckert2013b, |
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. @article{Rueckert2013, |
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. @article{Rueckert2012, |
Conferences |
Leonel, Rozo; Vedant, Dave Orientation Probabilistic Movement Primitives on Riemannian Manifolds Conference Conference on Robot Learning (CoRL), vol. 164, 2022. @conference{Leonel2022, Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabilistic movement primitives (ProMPs) stand out as a principled approach that models trajectory distributions learned from demonstrations. ProMPs allow for trajectory modulation and blending to achieve better generalization to novel situations. However, when ProMPs are employed in operational space, their original formulation does not directly apply to full-pose movements including rotational trajectories described by quaternions. This paper proposes a Riemannian formulation of ProMPs that enables encoding and retrieving of quaternion trajectories. Our method builds on Riemannian manifold theory, and exploits multilinear geodesic regression for estimating the ProMPs parameters. This novel approach makes ProMPs a suitable model for learning complex full-pose robot motion patterns. Riemannian ProMPs are tested on toy examples to illustrate their workflow, and on real learning-from-demonstration experiments. |
Akbulut, M Tuluhan; Oztop, Erhan; Seker, M Yunus; Xue, Honghu; Tekden, Ahmet E; Ugur, Emre 2020. @conference{nokey, To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). In this paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural Movement Primitives (ACNMP), that allows efficient policy improvement in novel environments and effective skill transfer between different agents. This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for acquiring the demonstrated trajectories and via RL for new trajectory discovery. Through simulation experiments, we show that (i) ACNMP enables the system to extrapolate to situations where pure LfD fails; (ii) Simultaneous training of the system through SL and RL preserves the shape of demonstrations while adapting to novel situations due to the shared representations used by both learners; (iii) ACNMP enables order-of-magnitude sample-efficient RL in extrapolation of reaching tasks compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of assumptions compared to the state-of-the-art approaches. Finally, we show the real-world suitability of ACNMPs through real robot experiments that involve obstacle avoidance, pick and place and pouring actions. |
Inproceedings |
Dave, Vedant; Rueckert, Elmar Predicting full-arm grasping motions from anticipated tactile responses Inproceedings In: International Conference on Humanoid Robots (Humanoids 2022), 2022. @inproceedings{Dave2022, Tactile sensing provides significant information about the state of the environment for performing manipulation tasks. Depending on the physical properties of the object, manipulation tasks can exhibit large variation in their movements. For a grasping task, the movement of the arm and of the end effector varies depending on different points of contact on the object, especially if the object is non-homogeneous in hardness and/or has an uneven geometry. In this paper, we propose Tactile Probabilistic Movement Primitives (TacProMPs), to learn a highly non-linear relationship between the desired tactile responses and the full-arm movement. We solely condition on the tactile responses to infer the complex manipulation skills. We formulate a joint trajectory of full-arm joints with tactile data, leverage the model to condition on the desired tactile response from the non-homogeneous object and infer the full-arm (7-dof panda arm and 19-dof gripper hand) motion. We use a Gaussian Mixture Model of primitives to address the multimodality in demonstrations. We also show that the measurement noise adjustment must be taken into account due to multiple systems working in collaboration. We validate and show the robustness of the approach through two experiments. First, we consider an object with non-uniform hardness. Grasping from different locations require different motion, and results into different tactile responses. Second, we have an object with homogeneous hardness, but we grasp it with widely varying grasping configurations. Our result shows that TacProMPs can successfully model complex multimodal skills and generalise to new situations. |
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 Inproceedings In: International Conference on Humanoid Robots (Humanoids 2022), 2022. @inproceedings{Xue2022b, We solve a visual navigation problem in an urban setting via deep reinforcement learning in an end-to-end manner. A major challenge of a first-person visual navigation problem lies in severe partial observability and sparse positive experiences of reaching the goal. To address partial observability, we propose a novel 3D-temporal convolutional network to encode sequential historical visual observations, its effectiveness is verified by comparing to a commonly-used frame-stacking approach. For sparse positive samples, we propose an improved automatic curriculum learning algorithm NavACL+, which proposes meaningful curricula starting from easy tasks and gradually generalizes to challenging ones. NavACL+ is shown to facilitate the learning process, greatly improve the task success rate on difficult tasks by at least 40% and offer enhanced generalization to different initial poses compared to training from a fixed initial pose and the original NavACL algorithm. |
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 Inproceedings In: International Conference on Advanced Robotics , pp. 7, 2021. @inproceedings{Denz2021, |
Rottmann, N.; Denz, R.; Bruder, R.; Rueckert, E. Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems Inproceedings In: European Conference on Mobile Robots (ECMR 2021), 2021. @inproceedings{Rottmann2021, |
Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E. Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors Inproceedings In: IEEE SENSORS , pp. 1–4, 2020. @inproceedings{Rottmann2020b, |
Rottmann, N.; Kunavar, T.; Babič, J.; Peters, J.; Rueckert, E. Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings In: International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020. @inproceedings{Rottmann2020HiBO, |
Rottmann, N.; Bruder, R.; Xue, H.; Schweikard, A.; Rueckert, E. Parameter Optimization for Loop Closure Detection in Closed Environments Inproceedings In: Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS), pp. 1–8, 2020. @inproceedings{Rottmann2020c, |
Tolga-Can Çallar, Elmar Rueckert; Böttger, Sven Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates Inproceedings In: 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020. @inproceedings{Çallar2020, |
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 In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. @inproceedings{Xue2020, |
Stark, Svenja; Peters, Jan; Rueckert, Elmar Experience Reuse with Probabilistic Movement Primitives Inproceedings In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019., 2019. @inproceedings{Stark2019, |
Boettger, S.; Callar, T. C.; Schweikard, A.; Rueckert, E. Medical robotics simulation framework for application-specific optimal kinematics Inproceedings In: Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019. @inproceedings{Boettger2019, |
Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E. Loop Closure Detection in Closed Environments Inproceedings In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9. @inproceedings{Rottmann2019b, |
Rottmann, N.; Bruder, R.; Schweikard, A.; Rueckert, E. Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Inproceedings In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019). @inproceedings{Rottmann2019, |
Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim Dynamic Control Strategies for Cable-Driven Master Slave Robots Inproceedings In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019). @inproceedings{Rueckert2019c, |
Gondaliya, Kaushikkumar D.; Peters, Jan; Rueckert, Elmar Learning to Categorize Bug Reports with LSTM Networks Inproceedings 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). @inproceedings{Gondaliya2018, |
Rueckert, Elmar; Nakatenus, Moritz; Tosatto, Samuele; Peters, Jan Learning Inverse Dynamics Models in O(n) time with LSTM networks Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. @inproceedings{Humanoids2017Rueckert, |
Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Efficient Online Adaptation with Stochastic Recurrent Neural Networks Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. @inproceedings{Tanneberg2017a, |
Stark, Svenja; Peters, Jan; Rueckert, Elmar A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. @inproceedings{Humanoids2017Stark, |
Thiem, Simon; Stark, Svenja; Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Simulation of the underactuated Sake Robotics Gripper in V-REP Inproceedings In: Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017. @inproceedings{Thiem2017b, |
Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Inproceedings In: Proceedings of the Conference on Robot Learning (CoRL), 2017. @inproceedings{Tanneberg2017, |
Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar Deep Spiking Networks for Model-based Planning in Humanoids Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. @inproceedings{tanneberg_humanoids16, |
Azad, Morteza; Ortenzi, Valerio; Lin, Hsiu-Chin; Rueckert, Elmar; Mistry, Michael Model Estimation and Control of Complaint Contact Normal Force Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. @inproceedings{Humanoids2016Azad, |
Kohlschuetter, Jan; Peters, Jan; Rueckert, Elmar Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities Inproceedings In: Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 2016. @inproceedings{Kohlschuetter2016, |
Modugno, Valerio; Neumann, Gerhard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena Learning soft task priorities for control of redundant robots Inproceedings In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2016. @inproceedings{Modugno_PICRA_2016, |
Sharma, David; Tanneberg, Daniel; Grosse-Wentrup, Moritz; Peters, Jan; Rueckert, Elmar Adaptive Training Strategies for BCIs Inproceedings In: Cybathlon Symposium, 2016. @inproceedings{Sharma2016, |
Weber, Paul; Rueckert, Elmar; Calandra, Roberto; Peters, Jan; Beckerle, Philipp In: Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016. @inproceedings{ROMANS16_daglove, |
Calandra, Roberto; Ivaldi, Serena; Deisenroth, Marc; Rueckert, Elmar; Peters, Jan Learning Inverse Dynamics Models with Contacts Inproceedings In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. @inproceedings{Calandra2015, |
Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. @inproceedings{Rueckert2015, |
Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard Model-Free Probabilistic Movement Primitives for Physical Interaction Inproceedings In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015. @inproceedings{Paraschos2015, |
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 In: ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015. @inproceedings{Rueckert2015b, |
Rueckert, Elmar; Mindt, Max; Peters, Jan; Neumann, Gerhard Robust Policy Updates for Stochastic Optimal Control Inproceedings In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014. @inproceedings{Rueckert2014, |
Rueckert, Elmar; d’Avella, Andrea Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems Inproceedings In: Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp. 27–28, 2013. @inproceedings{Rueckert2013, |
Rueckert, Elmar; Neumann, Gerhard A study of Morphological Computation by using Probabilistic Inference for Motor Planning Inproceedings In: Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp. 51–53, 2011. @inproceedings{Rueckert2011, |
Masters Theses |
Rueckert, Elmar Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis Technical University Graz, 2010. @mastersthesis{Rueckert2010, |
PhD Theses |
Rueckert, Elmar Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis Technical University Graz, 2014. @phdthesis{Rueckert2014a, |
Workshops |
Dave, Vedant; Rueckert, Elmar Can we infer the full-arm manipulation skills from tactile targets? Workshop International Conference on Humanoid Robots (Humanoids 2022), 2022. @workshop{Dave2022WS, Tactile sensing provides significant information about the state of the environment for performing manipulation tasks. Manipulation skills depends on the desired initial contact points between the object and the end-effector. Based on physical properties of the object, this contact results into distinct tactile responses. We propose Tactile Probabilistic Movement Primitives (TacProMPs), to learn a highly non-linear relationship between the desired tactile responses and the full-arm movement, where we condition solely on the tactile responses to infer the complex manipulation skills. We use a Gaussian mixture model of primitives to address the multimodality in demonstrations. We demonstrate the performance of our method in challenging real-world scenarios. |
Track Record
News
January 13, 2023
September 29, 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 20, 2022
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
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
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.scienceJuly 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 TrackFebruary 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.scienceApril 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 ModelsSeptember 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 roboticsFebruary 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 primitivesJanuary 28, 2010
M.Sc. defense – Summa Cum Laude (with honors).
At the technical University Graz with Prof. Horst Bischof.2 thoughts on “Univ.-Prof. Dr. Elmar Rueckert”
Comments are closed.
[…] Univ. Prof. Dr Elmar Rückert […]
[…] Univ. Prof. Dr Elmar Rückert […]