2021
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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}
}
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Rozo*, Leonel; Dave*, Vedant Orientation Probabilistic Movement Primitives on Riemannian Manifolds Conference Conference on Robot Learning, vol. 5, 2021. @conference{Rozo&Dave*2021,
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 = {2021},
date = {2021-11-08},
urldate = {2021-11-08},
booktitle = {Conference on Robot Learning},
volume = {5},
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 = {Manipulation, Probabilistic Movement Primitives, Riemannian Manifolds},
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. |  |
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 = {Manipulation, movement primitives, Probabilistic Inference},
pubstate = {published},
tppubtype = {article}
}
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2020
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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}
}
|  |
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 = {Manipulation, Simulation},
pubstate = {published},
tppubtype = {article}
}
|  |
2017
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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}
}
|  |
