2020
|
Rottmann, N; Kunavar, T; Babič, J; Peters, J; Rueckert, E Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings International Conference on Intelligent Robots and Systems (IROS’ 2020), 2020. BibTeX | Tags: Reinforcement Learning | Links:  @inproceedings{Rottmann2020HiBO,
title = {Learning Hierarchical Acquisition Functions for Bayesian Optimization},
author = {N. Rottmann and T. Kunavar and J. Babič and J. Peters and E. Rueckert},
url = {https://rob.ai-lab.science/wp/IROS2020Rottmann.pdf, Article File},
year = {2020},
date = {2020-10-25},
booktitle = {International Conference on Intelligent Robots and Systems (IROS’ 2020)},
keywords = {Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Tolga-Can Çallar, Elmar Rueckert ; Böttger, Sven Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates Inproceedings 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020. BibTeX | Tags: Medical Robotics | Links: @inproceedings{Çallar2020,
title = {Efficient Body Registration Using Single-View Range Imaging and Generic Shape Templates},
author = {Tolga-Can Çallar, Elmar Rueckert and Sven Böttger},
url = {https://ai-lab.science/wp/BMT2020Callar.pdf, Article File },
year = {2020},
date = {2020-09-20},
booktitle = {54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020)},
journal = {Current Directions in Biomedical Engineering by De Gruyter},
keywords = {Medical Robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
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 International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. BibTeX | Tags: Manipulation, Reinforcement Learning | Links: @inproceedings{Xue2020,
title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks},
author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},
url = {https://ai-lab.science/wp/ASPAI2020Xue.pdf, Article File},
year = {2020},
date = {2020-06-30},
booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)},
keywords = {Manipulation, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Cartoni, E; Mannella, F; Santucci, V G; Triesch, J; Rueckert, E; Baldassarre, G REAL-2019: Robot open-Ended Autonomous Learning competition Journal Article Proceedings of Machine Learning Research, 123 , pp. 142-152, 2020, (NeurIPS 2019 Competition and Demonstration Track). BibTeX | Tags: Manipulation, Simulation | Links: @article{Cartoni2020,
title = {REAL-2019: Robot open-Ended Autonomous Learning competition},
author = {E. Cartoni and F. Mannella and V.G. Santucci and J. Triesch and E. Rueckert and G. Baldassarre},
editor = {H. J. Escalante and R. Hadsell},
url = {https://ai-lab.science/wp/PMLR2020Cartoni.pdf, Article File},
year = {2020},
date = {2020-06-20},
journal = {Proceedings of Machine Learning Research},
volume = {123},
pages = {142-152},
note = {NeurIPS 2019 Competition and Demonstration Track},
keywords = {Manipulation, Simulation},
pubstate = {published},
tppubtype = {article}
}
|  |
2019
|
Stark, Svenja; Peters, Jan; Rueckert, Elmar Experience Reuse with Probabilistic Movement Primitives Inproceedings Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019., 2019. BibTeX | Tags: movement primitives, Reinforcement Learning, Transfer Learning | Links: @inproceedings{Stark2019,
title = {Experience Reuse with Probabilistic Movement Primitives},
author = {Svenja Stark and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/IROS2019Stark.pdf, Article File},
year = {2019},
date = {2019-11-03},
booktitle = {Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019.},
keywords = {movement primitives, Reinforcement Learning, Transfer Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Boettger, S; Callar, T C; Schweikard, A; Rueckert, E Medical robotics simulation framework for application-specific optimal kinematics Inproceedings Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019. BibTeX | Tags: Medical Robotics | Links: @inproceedings{Boettger2019,
title = {Medical robotics simulation framework for application-specific optimal kinematics},
author = {S. Boettger and T.C. Callar and A. Schweikard and E. Rueckert},
url = {https://ai-lab.science/wp/BMT2019Boettger.pdf, Article File},
year = {2019},
date = {2019-09-25},
booktitle = {Current Directions in Biomedical Engineering 2019},
pages = {1--5},
keywords = {Medical Robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E Loop Closure Detection in Closed Environments Inproceedings European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9. BibTeX | Tags: mobile navigation | Links: @inproceedings{Rottmann2019b,
title = {Loop Closure Detection in Closed Environments},
author = {N. Rottmann and R. Bruder and A. Schweikard and E. Rueckert},
url = {https://ai-lab.science/wp/ECMR2019Rottmann.pdf, Article File},
isbn = {978-1-7281-3605-9},
year = {2019},
date = {2019-09-04},
booktitle = {European Conference on Mobile Robots (ECMR 2019)},
keywords = {mobile navigation},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Rottmann, N; Bruder, R; Schweikard, A; Rueckert, E Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors Inproceedings Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019). BibTeX | Tags: constraint optimization, mobile navigation, Simulation | Links: @inproceedings{Rottmann2019,
title = {Cataglyphis ant navigation strategies solve the global localization problem in robots with binary sensors},
author = {N. Rottmann and R. Bruder and A. Schweikard and E. Rueckert},
url = {https://rob.ai-lab.science/wp/Biosignals2018Rottmann.pdf, Article File},
year = {2019},
date = {2019-02-22},
booktitle = {Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS)},
address = {Prague, Czech Republic },
note = { February 22-24, 2019},
keywords = {constraint optimization, mobile navigation, Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim Dynamic Control Strategies for Cable-Driven Master Slave Robots Inproceedings Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019). BibTeX | Tags: Medical Robotics, Reinforcement Learning | Links:  @inproceedings{Rueckert2019c,
title = {Dynamic Control Strategies for Cable-Driven Master Slave Robots},
author = {Elmar Rueckert and Philipp Jauer and Alexander Derksen and Achim Schweikard},
editor = {Tobias Keck},
doi = {10.18416/MIC.2019.1901007},
year = {2019},
date = {2019-01-24},
booktitle = {Proceedings on Minimally Invasive Surgery, Luebeck, Germany},
note = {January 24-25, 2019},
keywords = {Medical Robotics, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Journal Article Neural Networks - Elsevier, 109 , pp. 67-80, 2019, ISBN: 0893-6080, (Impact Factor of 7.197 (2017)). BibTeX | Tags: neural network, Probabilistic Inference, RNN, spiking | Links: @article{Tanneberg2019,
title = {Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks},
author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},
url = {https://rob.ai-lab.science/wp/NeuralNetworks2018Tanneberg.pdf, Article File},
doi = {10.1016/j.neunet.2018.10.005},
isbn = {0893-6080},
year = {2019},
date = {2019-01-01},
journal = {Neural Networks - Elsevier},
volume = {109},
pages = {67-80},
note = {Impact Factor of 7.197 (2017)},
keywords = {neural network, Probabilistic Inference, RNN, spiking},
pubstate = {published},
tppubtype = {article}
}
|  |
2018
|
Gondaliya, Kaushikkumar D; Peters, Jan; Rueckert, Elmar Learning to Categorize Bug Reports with LSTM Networks Inproceedings 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). BibTeX | Tags: Natural Language Processing, neural network, RNN | Links: @inproceedings{Gondaliya2018,
title = {Learning to Categorize Bug Reports with LSTM Networks},
author = {Kaushikkumar D. Gondaliya and Jan Peters and Elmar Rueckert},
url = {https://rob.ai-lab.science/wp/VALID2018Gondaliya.pdf, Article File},
isbn = {978-1-61208-671-2},
year = {2018},
date = {2018-10-14},
booktitle = {Proceedings of the International Conference on Advances in System Testing and Validation Lifecycle (VALID).},
pages = {6},
publisher = {XPS (Xpert Publishing Services)},
address = {Nice, France},
note = { October 14-18, 2018},
keywords = {Natural Language Processing, neural network, RNN},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Sosic, Adrian; Zoubir, Abdelhak M; Rueckert, Elmar; Peters, Jan; Koeppl, Heinz Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal Article Journal of Machine Learning Research (JMLR), 19 (69), pp. 1-45, 2018. BibTeX | Tags: movement primitives, Probabilistic Inference, Simulation | Links: @article{Sosic2018,
title = {Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling},
author = {Adrian Sosic and Abdelhak M. Zoubir and Elmar Rueckert and Jan Peters and
Heinz Koeppl},
url = {https://rob.ai-lab.science/wp/JMLR2018Sosic.pdf, Preprint Article File},
year = {2018},
date = {2018-10-08},
journal = {Journal of Machine Learning Research (JMLR)},
volume = {19},
number = {69},
pages = {1-45},
keywords = {movement primitives, Probabilistic Inference, Simulation},
pubstate = {published},
tppubtype = {article}
}
|  |
Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard Probabilistic Movement Primitives under Unknown System Dynamics Journal Article Advanced Robotics (ARJ), 32 (6), pp. 297-310, 2018. BibTeX | Tags: inverse dynamics, model learning, movement primitives | Links: @article{Paraschos2018,
title = {Probabilistic Movement Primitives under Unknown System Dynamics},
author = {Alexandros Paraschos and Elmar Rueckert and Jan Peters and Gerhard Neumann },
url = {https://rob.ai-lab.science/wp/AR2018Paraschos.pdf, Article File},
doi = {10.1080/01691864.2018.1437674},
year = {2018},
date = {2018-01-10},
journal = {Advanced Robotics (ARJ)},
volume = {32},
number = {6},
pages = {297-310},
keywords = {inverse dynamics, model learning, movement primitives},
pubstate = {published},
tppubtype = {article}
}
|  |
2017
|
Rueckert, Elmar; Nakatenus, Moritz; Tosatto, Samuele; Peters, Jan Learning Inverse Dynamics Models in O(n) time with LSTM networks Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. BibTeX | Tags: inverse dynamics, model learning, RNN | Links: @inproceedings{Humanoids2017Rueckert,
title = {Learning Inverse Dynamics Models in O(n) time with LSTM networks},
author = {Elmar Rueckert and Moritz Nakatenus and Samuele Tosatto and Jan Peters},
url = {https://ai-lab.science/wp/Humanoids2017Rueckert.pdf, Article File},
year = {2017},
date = {2017-11-15},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p11091},
key = {goal-robots, skills4robots},
keywords = {inverse dynamics, model learning, RNN},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Efficient Online Adaptation with Stochastic Recurrent Neural Networks Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. BibTeX | Tags: intrinsic motivation, RNN, spiking | Links: @inproceedings{Tanneberg2017a,
title = {Efficient Online Adaptation with Stochastic Recurrent Neural Networks},
author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/Humanoids2017Tanneberg.pdf, Article File},
year = {2017},
date = {2017-11-15},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p11092},
key = {goal-robots, skills4robots},
keywords = {intrinsic motivation, RNN, spiking},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Stark, Svenja; Peters, Jan; Rueckert, Elmar A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. BibTeX | Tags: intrinsic motivation, movement primitives | Links: @inproceedings{Humanoids2017Stark,
title = {A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries},
author = {Svenja Stark and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/Humanoids2017Stark.pdf, Article File},
year = {2017},
date = {2017-11-15},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p11093},
key = {goal-robots, skills4robots},
keywords = {intrinsic motivation, movement primitives},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Thiem, Simon; Stark, Svenja; Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Simulation of the underactuated Sake Robotics Gripper in V-REP Inproceedings Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017. BibTeX | Tags: Manipulation, Simulation | Links: @inproceedings{Thiem2017b,
title = {Simulation of the underactuated Sake Robotics Gripper in V-REP},
author = {Simon Thiem and Svenja Stark and Daniel Tanneberg and Jan Peters and Elmar Rueckert},
url = {https://rob.ai-lab.science/wp/Humanoids2017Thiem.pdf},
year = {2017},
date = {2017-11-15},
booktitle = {Workshop at the International Conference on Humanoid Robots (HUMANOIDS)},
keywords = {Manipulation, Simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Tanneberg, Daniel; Peters, Jan; Rueckert, Elmar Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals Inproceedings Proceedings of the Conference on Robot Learning (CoRL), 2017. BibTeX | Tags: intrinsic motivation, RNN, spiking | Links: @inproceedings{Tanneberg2017,
title = {Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals},
author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/CoRL2017Tanneberg.pdf, Article File},
year = {2017},
date = {2017-11-10},
booktitle = {Proceedings of the Conference on Robot Learning (CoRL)},
crossref = {p11088},
key = {goal-robots, skills4robots},
keywords = {intrinsic motivation, RNN, spiking},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2016
|
Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar Deep Spiking Networks for Model-based Planning in Humanoids Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. BibTeX | Tags: model learning, RNN, spiking | Links:  @inproceedings{tanneberg_humanoids16,
title = {Deep Spiking Networks for Model-based Planning in Humanoids},
author = {Daniel Tanneberg and Alexandros Paraschos and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/Humanoids2016Tanneberg.pdf, Article File
https://ai-lab.science/wp/resources/videos/humanoids_slower_540p.mp4, Supplementary Video},
year = {2016},
date = {2016-11-16},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p10980},
key = {codyco and tacman},
keywords = {model learning, RNN, spiking},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Azad, Morteza; Ortenzi, Valerio; Lin, Hsiu-Chin; Rueckert, Elmar; Mistry, Michael Model Estimation and Control of Complaint Contact Normal Force Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. BibTeX | Tags: constraint optimization, human motor control, inverse dynamics, model learning | Links: @inproceedings{Humanoids2016Azad,
title = {Model Estimation and Control of Complaint Contact Normal Force},
author = {Morteza Azad and Valerio Ortenzi and Hsiu-Chin Lin and Elmar Rueckert and Michael Mistry},
url = {https://ai-lab.science/wp/Humanoids2016Azad.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_LocallyWeightedRegression_MEX_2015Rueckert.zip, MATLAB Code (fast LWR MEX-Function Implementation)},
year = {2016},
date = {2016-11-16},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p10986},
key = {codyco},
keywords = {constraint optimization, human motor control, inverse dynamics, model learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article Nature Publishing Group: Scientific Reports, 6 (28455), 2016. BibTeX | Tags: human motor control, muscle synergies, postural control | Links: @article{Rueckert2016b,
title = {Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control},
author = {Elmar Rueckert and Jernej Camernik and Jan Peters and Jan Babic},
url = {https://ai-lab.science/wp/SciReps_HumanContacts.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip, MATLAB Code
https://rob.ai-lab.science/wp/SciReps_HumanContacts_Supplement.pdf, Supplement},
doi = {10.1038/srep28455},
year = {2016},
date = {2016-06-02},
journal = {Nature Publishing Group: Scientific Reports},
volume = {6},
number = {28455},
keywords = {human motor control, muscle synergies, postural control},
pubstate = {published},
tppubtype = {article}
}
|  |
Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan Recurrent Spiking Networks Solve Planning Tasks Journal Article Nature Publishing Group: Scientific Reports, 6 (21142), 2016. BibTeX | Tags: RNN, spiking | Links: @article{Rueckert2016a,
title = {Recurrent Spiking Networks Solve Planning Tasks},
author = {Elmar Rueckert and David Kappel and Daniel Tanneberg and Dejan Pecevski and Jan Peters},
url = {https://ai-lab.science/wp/SciReps_NeuralPlanning.pdf, Article File
https://rob.ai-lab.science/wp/SciReps_NeuralPlanning_Supplement.pdf, Supplement
https://ai-lab.science/wp/resources/code/MATLAB_SpikingNeuralPlanning_2016Rueckert.zip, MATLAB Code},
doi = {10.1038/srep21142},
year = {2016},
date = {2016-01-15},
journal = {Nature Publishing Group: Scientific Reports},
volume = {6},
number = {21142},
keywords = {RNN, spiking},
pubstate = {published},
tppubtype = {article}
}
|  |
Kohlschuetter, Jan; Peters, Jan; Rueckert, Elmar Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities Inproceedings Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON), 2016. BibTeX | Tags: graphical models, muscle synergies | Links: @inproceedings{Kohlschuetter2016,
title = {Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities},
author = {Jan Kohlschuetter and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/KohlschuetterMEDICON_2016.pdf, Article File},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the XIV Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON)},
crossref = {p10898},
key = {codyco, tacman},
keywords = {graphical models, muscle synergies},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Modugno, Valerio; Neumann, Gerhard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena Learning soft task priorities for control of redundant robots Inproceedings Proceedings of the International Conference on Robotics and Automation (ICRA), 2016. BibTeX | Tags: constraint optimization, policy search | Links: @inproceedings{Modugno_PICRA_2016,
title = {Learning soft task priorities for control of redundant robots},
author = {Valerio Modugno and Gerhard Neumann and Elmar Rueckert and Giuseppe Oriolo and Jan Peters and Serena Ivaldi},
url = {https://ai-lab.science/wp/ICRA2016Modugno.pdf, Article File},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
crossref = {p10900},
key = {codyco},
keywords = {constraint optimization, policy search},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Sharma, David; Tanneberg, Daniel; Grosse-Wentrup, Moritz; Peters, Jan; Rueckert, Elmar Adaptive Training Strategies for BCIs Inproceedings Cybathlon Symposium, 2016. BibTeX | Tags: human motor control, Reinforcement Learning | Links: @inproceedings{Sharma2016,
title = {Adaptive Training Strategies for BCIs},
author = {David Sharma and Daniel Tanneberg and Moritz Grosse-Wentrup and Jan Peters and Elmar Rueckert},
url = {https://ai-lab.science/wp/Cybathlon2016Sharma.pdf, Article File},
year = {2016},
date = {2016-01-01},
booktitle = {Cybathlon Symposium},
crossref = {p10952},
keywords = {human motor control, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
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 Inproceedings Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016. BibTeX | Tags: graphical models | Links: @inproceedings{ROMANS16_daglove,
title = {A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction},
author = {Paul Weber and Elmar Rueckert and Roberto Calandra and Jan Peters and Philipp Beckerle},
url = {https://ai-lab.science/wp/ROMANS2016Weber.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_Arduino_SensorGlove_2015Rueckert.zip, MATLAB Code (JAVA Interface), ARDUINO Firmware
https://ai-lab.science/wp/resources/code/MATLAB_SensorGloveMexInterface_2015Rueckert.zip, MATLAB Code (MEX-Function Demo Interface)},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)},
crossref = {p10949},
key = {codyco},
keywords = {graphical models},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2015
|
Calandra, Roberto; Ivaldi, Serena; Deisenroth, Marc; Rueckert, Elmar; Peters, Jan Learning Inverse Dynamics Models with Contacts Inproceedings Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. BibTeX | Tags: inverse dynamics, model learning, neural network | Links: @inproceedings{Calandra2015,
title = {Learning Inverse Dynamics Models with Contacts},
author = {Roberto Calandra and Serena Ivaldi and Marc Deisenroth and Elmar Rueckert and Jan Peters},
url = {https://ai-lab.science/wp/ICRA15Calandra.pdf, Article File},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
crossref = {p10794},
key = {codyco},
keywords = {inverse dynamics, model learning, neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard Extracting Low-Dimensional Control Variables for Movement Primitives Inproceedings Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. BibTeX | Tags: movement primitives, Probabilistic Inference | Links: @inproceedings{Rueckert2015,
title = {Extracting Low-Dimensional Control Variables for Movement Primitives},
author = {Elmar Rueckert and Jan Mundo and Alexandros Paraschos and Jan Peters and Gerhard Neumann},
url = {https://ai-lab.science/wp/ICRA2015Rueckert.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_LatentManifoldPrimitives_2015Rueckert.zip, MATALB Code
https://ai-lab.science/wp/resources/videos/KUKA_LatentManifold_ProMPs_REAL_Elmar.mp4, Supplementary Video},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
crossref = {p10796},
key = {3rdhand, codyco},
keywords = {movement primitives, Probabilistic Inference},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
Paraschos, Alexandros; Rueckert, Elmar; Peters, Jan; Neumann, Gerhard Model-Free Probabilistic Movement Primitives for Physical Interaction Inproceedings Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015. BibTeX | Tags: inverse dynamics, movement primitives | Links: @inproceedings{Paraschos2015,
title = {Model-Free Probabilistic Movement Primitives for Physical Interaction},
author = {Alexandros Paraschos and Elmar Rueckert and Jan Peters and Gerhard Neumann},
url = {https://ai-lab.science/wp/IROS2015Paraschos.pdf, Article File},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS)},
crossref = {p10832},
key = {codyco},
keywords = {inverse dynamics, movement primitives},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
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 ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015. BibTeX | Tags: graphical models | Links: @inproceedings{Rueckert2015b,
title = {Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations},
author = {Elmar Rueckert and Rudolf Lioutikov and Roberto Calandra and Marius Schmidt and Philipp Beckerle and Jan Peters},
url = {https://ai-lab.science/wp/ICRA2015Rueckertb.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_Arduino_SensorGlove_2015Rueckert.zip, MATLAB Code (JAVA Interface), ARDUINO Firmware
https://ai-lab.science/wp/resources/code/MATLAB_SensorGloveMexInterface_2015Rueckert.zip, MATLAB Code (MEX-Function Demo Interface)},
year = {2015},
date = {2015-01-01},
booktitle = {ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots},
crossref = {p10831},
key = {codyco},
keywords = {graphical models},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2014
|
Rueckert, Elmar Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis Technical University Graz, 2014. BibTeX | Tags: graphical models, locomotion, model learning, morphological compuation, movement primitives, policy search, postural control, Probabilistic Inference, Reinforcement Learning, RNN, SOC, spiking | Links: @phdthesis{Rueckert2014a,
title = {Biologically inspired motor skill learning in robotics through probabilistic inference},
author = {Elmar Rueckert},
url = {https://ai-lab.science/wp/PhDThesis2014Rueckert.pdf, Article File},
year = {2014},
date = {2014-02-04},
school = {Technical University Graz},
keywords = {graphical models, locomotion, model learning, morphological compuation, movement primitives, policy search, postural control, Probabilistic Inference, Reinforcement Learning, RNN, SOC, spiking},
pubstate = {published},
tppubtype = {phdthesis}
}
|  |
Rueckert, Elmar; Mindt, Max; Peters, Jan; Neumann, Gerhard Robust Policy Updates for Stochastic Optimal Control Inproceedings Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014. BibTeX | Tags: policy search, Probabilistic Inference, SOC | Links: @inproceedings{Rueckert2014,
title = {Robust Policy Updates for Stochastic Optimal Control},
author = {Elmar Rueckert and Max Mindt and Jan Peters and Gerhard Neumann},
url = {https://ai-lab.science/wp/Humanoids2014Rueckert.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_RobustStochasticOptimalControl_2015Rueckert.zip, MATLAB Code},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},
crossref = {p10768},
key = {codyco},
keywords = {policy search, Probabilistic Inference, SOC},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2013
|
Rueckert, Elmar; d'Avella, Andrea Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems Journal Article Frontiers in Computational Neuroscience, 7 (138), 2013. BibTeX | Tags: locomotion, movement primitives, muscle synergies | Links: @article{Rueckert2013b,
title = {Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems},
author = {Elmar Rueckert and Andrea d'Avella},
url = {https://ai-lab.science/wp/Frontiers2013bRueckert.pdf, Article File
https://ai-lab.science/wp/resources/code/MATLAB_PlanarWalkerSimulator_2013Rueckert.zip, MATLAB Code (Planar Walker Simulator)
https://ai-lab.science/wp/resources/code/MATLAB_OpenSimMEXInterface_2013Rueckert.zip, MATLAB Code (OpenSim MEX-Function Interface)},
doi = {10.3389/fncom.2013.00138},
year = {2013},
date = {2013-10-17},
journal = {Frontiers in Computational Neuroscience},
volume = {7},
number = {138},
keywords = {locomotion, movement primitives, muscle synergies},
pubstate = {published},
tppubtype = {article}
}
|  |
Rueckert, Elmar; Neumann, Gerhard; Toussaint, Marc; Maass, Wolfgang Learned graphical models for probabilistic planning provide a new class of movement primitives Journal Article Frontiers in Computational Neuroscience, 6 (97), 2013. BibTeX | Tags: graphical models, movement primitives, Probabilistic Inference | Links: @article{Rueckert2013,
title = { Learned graphical models for probabilistic planning provide a new class of movement primitives},
author = {Elmar Rueckert and Gerhard Neumann and Marc Toussaint and Wolfgang Maass},
url = {https://ai-lab.science/wp/Frontiers2013aRueckert.pdf, Article File},
doi = {10.3389/fncom.2012.00097},
year = {2013},
date = {2013-01-02},
journal = {Frontiers in Computational Neuroscience},
volume = {6},
number = {97},
keywords = {graphical models, movement primitives, Probabilistic Inference},
pubstate = {published},
tppubtype = {article}
}
|  |
Rueckert, Elmar; d'Avella, Andrea Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems Inproceedings Abstracts of Neural Control of Movement Conference (NCM), Conference Talk, pp. 27–28, 2013. BibTeX | Tags: muscle synergies, policy search, Reinforcement Learning | Links: @inproceedings{Rueckert2013,
title = {Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems},
author = {Elmar Rueckert and Andrea d'Avella},
url = {https://ai-lab.science/wp/Frontiers2013bRueckert.pdf, Article File},
year = {2013},
date = {2013-01-01},
booktitle = {Abstracts of Neural Control of Movement Conference (NCM), Conference Talk},
pages = {27--28},
crossref = {p10682},
keywords = {muscle synergies, policy search, Reinforcement Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2012
|
Rueckert, Elmar; Neumann, Gerhard Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation Journal Article Artificial Life, 19 (1), 2012. BibTeX | Tags: morphological compuation, Probabilistic Inference, SOC | Links: @article{Rueckert2012,
title = {Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation},
author = {Elmar Rueckert and Gerhard Neumann},
url = {https://ai-lab.science/wp/ArtificialLife2012Rueckert.pdf, Article File},
doi = {10.1162/ARTL_a_00085},
year = {2012},
date = {2012-11-27},
journal = {Artificial Life},
volume = {19},
number = {1},
keywords = {morphological compuation, Probabilistic Inference, SOC},
pubstate = {published},
tppubtype = {article}
}
|  |
2011
|
Rueckert, Elmar; Neumann, Gerhard A study of Morphological Computation by using Probabilistic Inference for Motor Planning Inproceedings Proceedings of the 2nd International Conference on Morphological Computation (ICMC), pp. 51–53, 2011. BibTeX | Tags: graphical models, morphological compuation, Probabilistic Inference, SOC | Links: @inproceedings{Rueckert2011,
title = {A study of Morphological Computation by using Probabilistic Inference for Motor Planning},
author = {Elmar Rueckert and Gerhard Neumann},
url = {https://ai-lab.science/wp/ICMC2011Rueckert.pdf, Article File},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 2nd International Conference on Morphological Computation (ICMC)},
pages = {51--53},
crossref = {p10680},
keywords = {graphical models, morphological compuation, Probabilistic Inference, SOC},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2010
|
Rueckert, Elmar Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis Technical University Graz, 2010. BibTeX | Tags: Probabilistic Inference | Links: @mastersthesis{Rueckert2010,
title = {Simultaneous localisation and mapping for mobile robots with recent sensor technologies},
author = {Elmar Rueckert},
url = {https://ai-lab.science/wp/MScThesis2009Rueckert.pdf, Article File},
year = {2010},
date = {2010-01-28},
school = {Technical University Graz},
keywords = {Probabilistic Inference},
pubstate = {published},
tppubtype = {mastersthesis}
}
|  |