Journal Articles
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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,
title = {Cerebellar transcranial current stimulation-an intraindividual comparison of different techniques},
author = {Rebecca Herzog and Till M Berger and Martje Gesine Pauly and Honghu Xue and Elmar Rueckert and Alexander Munchau and Tobias B{"a}umer and Anne Weissbach},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/4qoooTyjFfBwYEZ},
doi = {10.3389/fnins.2022.987472},
year = {2022},
date = {2022-09-19},
urldate = {2022-09-19},
journal = {Frontiers in Neuroscience},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {ROS-Mobile: An Android™ application for the Robot Operating System},
author = {Nils Rottmann and Nico Studt and Floris Ernst and Elmar Rueckert},
url = {https://arxiv.org/pdf/2011.02781.pdf},
doi = {10.48550/arXiv.2011.02781},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
journal = {Arxiv},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics},
author = {Honghu Xue and Benedikt Hein and Mohamed Bakr and Georg Schildbach and Bengt Abel and Elmar Rueckert},
editor = {/},
url = {https://cloud.cps.unileoben.ac.at/index.php/s/yddDZ7z9oqxenCi
},
year = {2022},
date = {2022-01-31},
urldate = {2022-01-31},
journal = {Applied Sciences (MDPI), Special Issue on Intelligent Robotics},
abstract = {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.},
note = {Supplement: https://cloud.cps.unileoben.ac.at/index.php/s/Sj68rQewnkf4ppZ},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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,
title = {Using Probabilistic Movement Primitives in analyzing human motion differences under Transcranial Current Stimulation},
author = {Honghu Xue and Rebecca Herzog and Till M. Berger and Tobias Bäumer and Anne Weissbach and Elmar Rueckert},
editor = {Kensuke Harada},
url = {https://cps.unileoben.ac.at/wp/Frontiers2021Xue.pdf},
issn = {2296-9144},
year = {2021},
date = {2021-08-14},
urldate = {2021-08-14},
journal = {Frontiers in Robotics and AI },
volume = {8},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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,
title = {SKID RAW: Skill Discovery from Raw Trajectories},
author = {Daniel Tanneberg and Kai Ploeger and Elmar Rueckert and Jan Peters },
url = {https://cps.unileoben.ac.at/wp/RAL2021Tanneberg.pdf, Article File},
issn = {2377-3766},
year = {2021},
date = {2021-03-10},
journal = {IEEE Robotics and Automation Letters (RA-L)},
pages = {1--8},
note = {© 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller},
author = {Marko Jamsek and Tjasa Kunavar and Urban Bobek and Elmar Rueckert and Jan Babic},
url = {../wp/RAL2021Jamsek.pdf, Article File},
doi = {10.1109/LRA.2021.3068892},
issn = {2377-3766},
year = {2021},
date = {2021-03-10},
journal = {IEEE Robotics and Automation Letters (RA-L)},
pages = {1--8},
note = {© 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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Interactive Human-Robot Skill Transfer: A Review of Learning Methods and User Experience},
author = {Mehmet Ege Cansev and Honghu Xue and Nils Rottmann and Adna Bliek and Luke E. Miller and Elmar Rueckert and Philipp Beckerle},
url = {https://cps.unileoben.ac.at/wp/AIS2021Cansev.pdf, Article File},
doi = {10.1002/aisy.202000247},
year = {2021},
date = {2021-03-10},
journal = {Advanced Intelligent Systems},
pages = {1--28},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {A novel Chlorophyll Fluorescence based approach for Mowing Area Classification},
author = {N. Rottmann and R. Bruder and A. Schweikard and E. Rueckert},
url = {https://cps.unileoben.ac.at/wp/IEEESensorsJournal2020Rottmann.pdf, Article File},
doi = {10.1109/JSEN.2020.3032722},
year = {2020},
date = {2020-10-12},
journal = {IEEE Sensors Journal},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Evolutionary training and abstraction yields algorithmic generalization of neural computers},
author = {Daniel Tanneberg and Elmar Rueckert and Jan Peters },
url = {https://rdcu.be/caRlg, Article File},
doi = {10.1038/s42256-020-00255-1},
year = {2020},
date = {2020-10-10},
journal = {Nature Machine Intelligence},
pages = {1--11},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
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://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
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://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
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://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Probabilistic Movement Primitives under Unknown System Dynamics},
author = {Alexandros Paraschos and Elmar Rueckert and Jan Peters and Gerhard Neumann },
url = {https://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
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://cps.unileoben.ac.at/wp/SciReps_HumanContacts.pdf, Article File
https://cps.unileoben.ac.at/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip, MATLAB Code
https://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Recurrent Spiking Networks Solve Planning Tasks},
author = {Elmar Rueckert and David Kappel and Daniel Tanneberg and Dejan Pecevski and Jan Peters},
url = {https://cps.unileoben.ac.at/wp/SciReps_NeuralPlanning.pdf, Article File
https://cps.unileoben.ac.at/wp/SciReps_NeuralPlanning_Supplement.pdf, Supplement
https://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems},
author = {Elmar Rueckert and Andrea d'Avella},
url = {https://cps.unileoben.ac.at/wp/Frontiers2013bRueckert.pdf, Article File
https://cps.unileoben.ac.at/wp/resources/code/MATLAB_PlanarWalkerSimulator_2013Rueckert.zip, MATLAB Code (Planar Walker Simulator)
https://cps.unileoben.ac.at/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 = {},
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 In: Frontiers in Computational Neuroscience, vol. 6, no. 97, 2013. @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://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
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,
title = {Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation},
author = {Elmar Rueckert and Gerhard Neumann},
url = {https://cps.unileoben.ac.at/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 = {},
pubstate = {published},
tppubtype = {article}
}
|
Conferences
|
Leonel, Rozo; Vedant, Dave Orientation Probabilistic Movement Primitives on Riemannian Manifolds Conference Conference on Robot Learning (CoRL), vol. 164, 2022. @conference{Leonel2022,
title = {Orientation Probabilistic Movement Primitives on Riemannian Manifolds},
author = {Leonel, Rozo and Vedant, Dave},
url = {https://cps.unileoben.ac.at/wp/orientation_probabilistic_move.pdf, Article File},
year = {2022},
date = {2022-01-11},
urldate = {2022-01-11},
booktitle = {Conference on Robot Learning (CoRL)},
volume = {164},
pages = {11},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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 ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing Conference 2020. @conference{nokey,
title = {ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing},
author = {M Tuluhan Akbulut and Erhan Oztop and M Yunus Seker and Honghu Xue and Ahmet E Tekden and Emre Ugur},
url = {https://cps.unileoben.ac.at/wp/CoRL2020Akbulut.pdf},
year = {2020},
date = {2020-11-20},
urldate = {2020-11-20},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
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,
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
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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,
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 = {},
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. |
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
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 = {},
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 = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
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 = {},
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 = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
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 = {},
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 = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
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 = {},
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 = {},
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 = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
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 = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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 = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Masters Theses
|
Rueckert, Elmar Simultaneous localisation and mapping for mobile robots with recent sensor technologies Masters Thesis Technical University Graz, 2010. @mastersthesis{Rueckert2010,
title = {Simultaneous localisation and mapping for mobile robots with recent sensor technologies},
author = {Elmar Rueckert},
url = {https://cps.unileoben.ac.at/wp/MScThesis2009Rueckert.pdf, Article File},
year = {2010},
date = {2010-01-28},
school = {Technical University Graz},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
|
PhD Theses
|
Rueckert, Elmar Biologically inspired motor skill learning in robotics through probabilistic inference PhD Thesis Technical University Graz, 2014. @phdthesis{Rueckert2014a,
title = {Biologically inspired motor skill learning in robotics through probabilistic inference},
author = {Elmar Rueckert},
url = {https://cps.unileoben.ac.at/wp/PhDThesis2014Rueckert.pdf, Article File},
year = {2014},
date = {2014-02-04},
school = {Technical University Graz},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
|
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,
title = {Can we infer the full-arm manipulation skills from tactile targets?},
author = {Vedant Dave and Elmar Rueckert},
url = {https://cloud.cps.unileoben.ac.at/index.php/f/562953},
year = {2022},
date = {2022-11-28},
urldate = {2022-11-28},
publisher = {International Conference on Humanoid Robots (Humanoids 2022)},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
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. |
[…] Univ. Prof. Dr Elmar Rückert […]
[…] Univ. Prof. Dr Elmar Rückert […]