2022
|
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,
title = {End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments},
author = {Honghu Xue and Rui Song and Julian Petzold and Benedikt Hein and Heiko Hamann and Elmar Rueckert},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/RzMQWqsFarQ6Kw4},
year = {2022},
date = {2022-09-26},
urldate = {2022-09-26},
publisher = {International Conference on Humanoid Robots (Humanoids 2022)},
abstract = {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.},
keywords = {Autonomous Navigation, Deep Learning, mobile navigation},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |  |
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,
title = {Predicting full-arm grasping motions from anticipated tactile responses},
author = {Vedant Dave and Elmar Rueckert},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/WzGSNtc5WRLN3EL},
year = {2022},
date = {2022-09-26},
urldate = {2022-09-26},
publisher = {International Conference on Humanoid Robots (Humanoids 2022)},
abstract = {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.},
keywords = {Grasping, Manipulation, Probabilistic Movement Primitives, Tactile Sensing},
pubstate = {published},
tppubtype = {inproceedings}
}
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. |  |
2021
|
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,
title = {A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning},
author = {R. Denz and R. Demirci and E. Cansev and A. Bliek and P. Beckerle and E. Rueckert and N. Rottmann},
url = {https://cps.unileoben.ac.at/wp/ICAR2021Denz.pdf, Article File},
year = {2021},
date = {2021-12-06},
booktitle = {International Conference on Advanced Robotics },
pages = {7},
keywords = {Grasping, human motor control, Manipulation, smart sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
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,
title = {Probabilistic Approach for Complete Coverage Path Planning with low-cost Systems},
author = {N. Rottmann and R. Denz and R. Bruder and E. Rueckert},
url = {https://cps.unileoben.ac.at/wp/ECMR2021Rottmann.pdf, Article File},
year = {2021},
date = {2021-08-31},
booktitle = {European Conference on Mobile Robots (ECMR 2021)},
keywords = {mobile navigation, Probabilistic Inference},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
2020
|
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,
title = {Exploiting Chlorophyll Fluorescense for Building Robust low-Cost Mowing Area Detectors},
author = {N. Rottmann and R. Bruder and A. Schweikard and E. Rueckert},
url = {https://cps.unileoben.ac.at/wp/IEEESensors2020Rottmann.pdf, Article File},
year = {2020},
date = {2020-10-26},
booktitle = {IEEE SENSORS },
journal = { IEEE SENSORS 2020 Conference, to be held from October 25-28, 2020},
pages = {1--4},
keywords = {mobile navigation, smart sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|  |
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,
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://cps.unileoben.ac.at/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}
}
|  |
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,
title = {Parameter Optimization for Loop Closure Detection in Closed Environments},
author = {N. Rottmann and R. Bruder and H. Xue and A. Schweikard and E. Rueckert},
url = {https://cps.unileoben.ac.at/wp/IROSWS2020Rottmann.pdf, Article File},
year = {2020},
date = {2020-10-25},
booktitle = {Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS)},
pages = {1--8},
keywords = {mobile navigation, 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 In: 54th Annual Conference of the German Society for Biomedical Engineering (BMT 2020), 2020. @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://cps.unileoben.ac.at/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 In: International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. @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://cps.unileoben.ac.at/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}
}
|  |
2019
|
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,
title = {Experience Reuse with Probabilistic Movement Primitives},
author = {Svenja Stark and Jan Peters and Elmar Rueckert},
url = {https://cps.unileoben.ac.at/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 In: Current Directions in Biomedical Engineering 2019, pp. 1–5, 2019. @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://cps.unileoben.ac.at/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 In: European Conference on Mobile Robots (ECMR 2019), 2019, ISBN: 978-1-7281-3605-9. @inproceedings{Rottmann2019b,
title = {Loop Closure Detection in Closed Environments},
author = {N. Rottmann and R. Bruder and A. Schweikard and E. Rueckert},
url = {https://cps.unileoben.ac.at/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 In: Proceedings of International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Prague, Czech Republic , 2019, ( February 22-24, 2019). @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://cps.unileoben.ac.at/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 In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019). @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}
}
|  |
2018
|
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,
title = {Learning to Categorize Bug Reports with LSTM Networks},
author = {Kaushikkumar D. Gondaliya and Jan Peters and Elmar Rueckert},
url = {https://cps.unileoben.ac.at/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}
}
|  |
2017
|
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,
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://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. @inproceedings{Tanneberg2017a,
title = {Efficient Online Adaptation with Stochastic Recurrent Neural Networks},
author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},
url = {https://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2017. @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://cps.unileoben.ac.at/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 In: Workshop at the International Conference on Humanoid Robots (HUMANOIDS), 2017. @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://cps.unileoben.ac.at/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 In: Proceedings of the Conference on Robot Learning (CoRL), 2017. @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://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. @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://cps.unileoben.ac.at/wp/Humanoids2016Tanneberg.pdf, Article File
https://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016. @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://cps.unileoben.ac.at/wp/Humanoids2016Azad.pdf, Article File
https://cps.unileoben.ac.at/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}
}
|  |
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,
title = {Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities},
author = {Jan Kohlschuetter and Jan Peters and Elmar Rueckert},
url = {https://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2016. @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://cps.unileoben.ac.at/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 In: Cybathlon Symposium, 2016. @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://cps.unileoben.ac.at/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 In: Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016. @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://cps.unileoben.ac.at/wp/ROMANS2016Weber.pdf, Article File
https://cps.unileoben.ac.at/wp/resources/code/MATLAB_Arduino_SensorGlove_2015Rueckert.zip, MATLAB Code (JAVA Interface), ARDUINO Firmware
https://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. @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://cps.unileoben.ac.at/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 In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015. @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://cps.unileoben.ac.at/wp/ICRA2015Rueckert.pdf, Article File
https://cps.unileoben.ac.at/wp/resources/code/MATLAB_LatentManifoldPrimitives_2015Rueckert.zip, MATALB Code
https://cps.unileoben.ac.at/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 In: Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2015. @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://cps.unileoben.ac.at/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 In: ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots, 2015. @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://cps.unileoben.ac.at/wp/ICRA2015Rueckertb.pdf, Article File
https://cps.unileoben.ac.at/wp/resources/code/MATLAB_Arduino_SensorGlove_2015Rueckert.zip, MATLAB Code (JAVA Interface), ARDUINO Firmware
https://cps.unileoben.ac.at/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; 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,
title = {Robust Policy Updates for Stochastic Optimal Control},
author = {Elmar Rueckert and Max Mindt and Jan Peters and Gerhard Neumann},
url = {https://cps.unileoben.ac.at/wp/Humanoids2014Rueckert.pdf, Article File
https://cps.unileoben.ac.at/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 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,
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://cps.unileoben.ac.at/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}
}
|  |
2011
|
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,
title = {A study of Morphological Computation by using Probabilistic Inference for Motor Planning},
author = {Elmar Rueckert and Gerhard Neumann},
url = {https://cps.unileoben.ac.at/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}
}
|  |
