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ORCIDElmar Rueckert
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- affiliation: University of Lübeck, Institute for Robotics and Cognitive Systems, Germany
- affiliation: TU Darmstadt, Intelligent Autonomous Systems Lab, Germany
- affiliation: Graz University of Technology, Institute for Theoretical Computer Science, Austria
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2020 – today
- 2024
[j12]Tjasa Kunavar
, Marko Jamsek, Edwin Johnatan Avila Mireles, Elmar Rueckert, Luka Peternel, Jan Babic:
The Effects of Different Motor Teaching Strategies on Learning a Complex Motor Task. Sensors 24(4): 1231 (2024)- [c29]Linus Nwankwo, Elmar Rueckert:
The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language. HRI (Companion) 2024: 808-812 - [c28]Vedant Dave, Fotios Lygerakis, Elmar Rueckert:
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training. ICRA 2024: 8013-8020 - [c27]Nikolaus Feith, Elmar Rueckert:
Advancing Interactive Robot Learning: A User Interface Leveraging Mixed Reality and Dual Quaternions. UR 2024: 21-26 - [c26]Melanie Neubauer, Elmar A. Rückert:
Semi-Autonomous Fast Object Segmentation and Tracking Tool for Industrial Applications. UR 2024: 77-83 - [c25]Nikolaus Feith, Elmar Rueckert:
Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement. UR 2024: 220-226 - [c24]Fotios Lygerakis, Vedant Dave, Elmar Rueckert:
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation. UR 2024: 490-497 - [i24]Linus Nwankwo, Elmar Rueckert:
The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language. CoRR abs/2401.11838 (2024) - [i23]Vedant Dave, Fotios Lygerakis, Elmar Rueckert:
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training. CoRR abs/2401.12024 (2024) - [i22]Nikolaus Feith, Elmar Rueckert:
Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement. CoRR abs/2401.12662 (2024) - [i21]Fotios Lygerakis, Vedant Dave, Elmar Rueckert:
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation. CoRR abs/2401.17032 (2024) - [i20]Linus Nwankwo, Elmar Rueckert:
Multimodal Human-Autonomous Agents Interaction Using Pre-Trained Language and Visual Foundation Models. CoRR abs/2403.12273 (2024) - [i19]Fotios Lygerakis, Elmar Rueckert:
ED-VAE: Entropy Decomposition of ELBO in Variational Autoencoders. CoRR abs/2407.06797 (2024) - [i18]Linus Nwankwo, Bjoern Ellensohn, Vedant Dave, Peter Hofer, Jan Forstner, Marlene Villneuve, Robert Galler, Elmar Rueckert:
EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments. CoRR abs/2410.22200 (2024) - 2023
- [c23]Harsh Yadav, Honghu Xue, Yan Rudall, Mohamed Bakr, Benedikt Hein, Elmar Rueckert, Ngoc Thinh Nguyen:
Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot. ICSTCC 2023: 283-288 - [i17]Nwankwo Linus, Elmar Rueckert:
Understanding why SLAM algorithms fail in modern indoor environments. CoRR abs/2305.05313 (2023) - [i16]Fotios Lygerakis, Elmar Rueckert:
CR-VAE: Contrastive Regularization on Variational Autoencoders for Preventing Posterior Collapse. CoRR abs/2309.02968 (2023) - 2022
- [c22]Honghu Xue, Rui Song, Julian Petzold, Benedikt Hein, Heiko Hamann, Elmar Rueckert:
End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments. Humanoids 2022: 350-357 - [c21]Vedant Dave, Elmar Rueckert:
Predicting full-arm grasping motions from anticipated tactile responses. Humanoids 2022: 464-471 - [i15]Honghu Xue, Benedikt Hein, Mohamed Bakr, Georg Schildbach, Bengt Abel, Elmar Rueckert:
Using Deep Reinforcement Learning with Automatic Curriculum earning for Mapless Navigation in Intralogistics. CoRR abs/2202.11512 (2022) - [i14]Nwankwo Linus, Fritze Clemens, Konrad Bartsch, Elmar Rueckert:
O2S: Open-source open shuttle. CoRR abs/2210.01627 (2022) - 2021
- [j11]Mehmet Ege Cansev, Honghu Xue, Nils Rottmann, Adna Bliek, Luke E. Miller, Elmar Rueckert, Philipp Beckerle:
Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience. Adv. Intell. Syst. 3(7): 2000247 (2021) - [j10]Honghu Xue, Rebecca Herzog, Till M. Berger, Tobias Bäumer, Anne Weissbach, Elmar Rueckert:
Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation. Frontiers Robotics AI 8: 721890 (2021) - [j9]Marko Jamsek, Tjasa Kunavar, Urban Bobek, Elmar Rueckert, Jan Babic:
Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined With a Flow Controller. IEEE Robotics Autom. Lett. 6(3): 4417-4424 (2021) - [j8]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery From Raw Trajectories. IEEE Robotics Autom. Lett. 6(3): 4696-4703 (2021) - [c20]Nils Rottmann, Robin Denz, Ralf Bruder, Elmar Rueckert:
A Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems. ECMR 2021: 1-8 - [c19]Robin Denz, Rabia Demirci, Mehmet Ege Cansev, Adna Bliek, Philipp Beckerle, Elmar Rueckert, Nils Rottmann:
A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning. ICAR 2021: 1109-1115 - [e1]Stefan Edelkamp, Ralf Möller, Elmar Rueckert:
KI 2021: Advances in Artificial Intelligence - 44th German Conference on AI, Virtual Event, September 27 - October 1, 2021, Proceedings. Lecture Notes in Computer Science 12873, Springer 2021, ISBN 978-3-030-87625-8 [contents] - [i13]Daniel Tanneberg, Kai Ploeger, Elmar Rueckert, Jan Peters:
SKID RAW: Skill Discovery from Raw Trajectories. CoRR abs/2103.14610 (2021) - [i12]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers. CoRR abs/2105.07957 (2021) - [i11]Honghu Xue, Rebecca Herzog, Till M. Berger, Tobias Bäumer, Anne Weissbach, Elmar Rueckert:
Using Probabilistic Movement Primitives in Analyzing Human Motion Difference under Transcranial Current Stimulation. CoRR abs/2107.02063 (2021) - 2020
- [j7]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary training and abstraction yields algorithmic generalization of neural computers. Nat. Mach. Intell. 2(12): 753-763 (2020) - [c18]Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert:
Exploiting Chlorophyll Fluorescense for building robust low-cost Mowing Area Detectors. IEEE SENSORS 2020: 1-4 - [c17]Nils Rottmann, Tjasa Kunavar, Jan Babic, Jan Peters, Elmar Rueckert:
Learning Hierarchical Acquisition Functions for Bayesian Optimization. IROS 2020: 5490-5496 - [i10]Nils Rottmann, Nico Studt, Floris Ernst, Elmar Rueckert:
ROS-Mobile: An Android application for the Robot Operating System. CoRR abs/2011.02781 (2020) - [i9]Nils Rottmann, Ralf Bruder, Honghu Xue, Achim Schweikard, Elmar Rueckert:
Parameter Optimization for Loop Closure Detection in Closed Environments. CoRR abs/2011.06286 (2020)
2010 – 2019
- 2019
- [j6]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks. Neural Networks 109: 67-80 (2019) - [c16]Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert:
Cataglyphis Ant Navigation Strategies Solve the Global Localization Problem in Robots with Binary Sensors. BIOSIGNALS 2019: 214-223 - [c15]Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert:
Loop Closure Detection in Closed Environments. ECMR 2019: 1-8 - [c14]Svenja Stark, Jan Peters, Elmar Rueckert:
Experience Reuse with Probabilistic Movement Primitives. IROS 2019: 1210-1217 - [c13]Emilio Cartoni, Francesco Mannella, Vieri Giuliano Santucci, Jochen Triesch, Elmar Rueckert, Gianluca Baldassarre:
REAL-2019: Robot open-Ended Autonomous Learning competition. NeurIPS (Competition and Demos) 2019: 142-152 - [i8]Zinan Liu, Kai Ploeger, Svenja Stark, Elmar Rueckert, Jan Peters:
Learning walk and trot from the same objective using different types of exploration. CoRR abs/1904.12336 (2019) - [i7]Svenja Stark, Jan Peters, Elmar Rueckert:
Experience Reuse with Probabilistic Movement Primitives. CoRR abs/1908.03936 (2019) - [i6]Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert:
Loop Closure Detection in Closed Environments. CoRR abs/1908.04558 (2019) - [i5]Nils Rottmann, Ralf Bruder, Achim Schweikard, Elmar Rueckert:
Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors. CoRR abs/1908.04564 (2019) - [i4]Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer. CoRR abs/1911.00926 (2019) - 2018
- [j5]Alexandros Paraschos, Elmar Rueckert, Jan Peters, Gerhard Neumann:
Probabilistic movement primitives under unknown system dynamics. Adv. Robotics 32(6): 297-310 (2018) - [j4]Adrian Sosic, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl:
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling. J. Mach. Learn. Res. 19: 69:1-69:45 (2018) - [i3]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks. CoRR abs/1802.08013 (2018) - [i2]Adrian Sosic, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl:
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling. CoRR abs/1803.00444 (2018) - 2017
- [c12]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals. CoRL 2017: 167-174 - [c11]Daniel Tanneberg, Jan Peters, Elmar Rueckert:
Efficient online adaptation with stochastic recurrent neural networks. Humanoids 2017: 198-204 - [c10]Svenja Stark, Jan Peters, Elmar Rueckert:
A comparison of distance measures for learning nonparametric motor skill libraries. Humanoids 2017: 624-630 - [c9]Elmar Rueckert, Moritz Nakatenus, Samuele Tosatto, Jan Peters:
Learning inverse dynamics models in O(n) time with LSTM networks. Humanoids 2017: 811-816 - 2016
- [c8]Morteza Azad, Valerio Ortenzi, Hsiu-Chin Lin, Elmar Rueckert, Michael N. Mistry:
Model estimation and control of compliant contact normal force. Humanoids 2016: 442-447 - [c7]Daniel Tanneberg, Alexandros Paraschos, Jan Peters, Elmar Rueckert:
Deep spiking networks for model-based planning in humanoids. Humanoids 2016: 656-661 - [c6]Valerio Modugno, Gerhard Neumann, Elmar Rueckert, Giuseppe Oriolo, Jan Peters, Serena Ivaldi:
Learning soft task priorities for control of redundant robots. ICRA 2016: 221-226 - [c5]Paul Weber, Elmar Rueckert, Roberto Calandra, Jan Peters, Philipp Beckerle:
A low-cost sensor glove with vibrotactile feedback and multiple finger joint and hand motion sensing for human-robot interaction. RO-MAN 2016: 99-104 - 2015
- [c4]Elmar Rueckert, Jan Mundo, Alexandros Paraschos, Jan Peters, Gerhard Neumann:
Extracting low-dimensional control variables for movement primitives. ICRA 2015: 1511-1518 - [c3]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Elmar Rueckert, Jan Peters:
Learning inverse dynamics models with contacts. ICRA 2015: 3186-3191 - [c2]Alexandros Paraschos, Elmar Rueckert, Jan Peters, Gerhard Neumann:
Model-free Probabilistic Movement Primitives for physical interaction. IROS 2015: 2860-2866 - [i1]Elmar Rueckert, Rudolf Lioutikov, Roberto Calandra, Marius Schmidt, Philipp Beckerle, Jan Peters:
Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations. CoRR abs/1510.03253 (2015) - 2014
- [c1]Elmar Rueckert, Max Mindt, Jan Peters, Gerhard Neumann:
Robust policy updates for stochastic optimal control. Humanoids 2014: 388-393 - 2013
- [j3]Elmar A. Rückert, Gerhard Neumann:
Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation. Artif. Life 19(1): 115-131 (2013) - [j2]Elmar Rueckert, Andrea d'Avella:
Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers Comput. Neurosci. 7: 138 (2013) - 2012
- [j1]Elmar A. Rückert, Gerhard Neumann, Marc Toussaint, Wolfgang Maass:
Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers Comput. Neurosci. 6: 97 (2012)
Coauthor Index
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