Tanneberg, Daniel; Ploeger, Kai; Rueckert, Elmar; Peters, Jan SKID RAW: Skill Discovery from Raw Trajectories Journal Article IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, (© 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.). Links | BibTeX | Tags: Manipulation, Movement Primitives, University of Luebeck @article{Tanneberg2021,
title = {SKID RAW: Skill Discovery from Raw Trajectories},
author = {Daniel Tanneberg and Kai Ploeger and Elmar Rueckert and Jan Peters },
url = {https://ai-lab.science/wp/RAL2021Tanneberg.pdf, Article File},
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, University of Luebeck},
pubstate = {published},
tppubtype = {article}
}
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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 IEEE Robotics and Automation Letters (RA-L), pp. 1–8, 2021, (© 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.). Links | BibTeX | Tags: Human Motor Control, Movement Primitives, University of Luebeck @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 = {https://ai-lab.science/wp/RAL2021Jamsek.pdf, Article File},
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 = {Human Motor Control, Movement Primitives, University of Luebeck},
pubstate = {published},
tppubtype = {article}
}
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Rottmann, N; Pandya, H; Bruder, R; Neumann, G; Schweikard, A; Rueckert, E Learning Hierarchical Acquisition Functions for Bayesian Optimization Inproceedings International Conference on Intelligent Robots and Systems (IROS 2020), pp. 1–8, 2020. Links | BibTeX | Tags: Reinforcement Learning, University of Luebeck @inproceedings{Rottmann2020,
title = {Learning Hierarchical Acquisition Functions for Bayesian Optimization},
author = {N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},
url = {https://rob.ai-lab.science/wp/IROS2020Rottmann.pdf, Article File},
year = {2020},
date = {2020-10-26},
booktitle = {International Conference on Intelligent Robots and Systems (IROS 2020)},
pages = {1--8},
keywords = {Reinforcement Learning, University of Luebeck},
pubstate = {published},
tppubtype = {inproceedings}
}
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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 International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020), 2020. Links | BibTeX | Tags: Manipulation, Reinforcement Learning, University of Luebeck @inproceedings{Xue2020,
title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks},
author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},
url = {https://ai-lab.science/wp/ASPAI2020Xue.pdf, Article File},
year = {2020},
date = {2020-06-30},
booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)},
keywords = {Manipulation, Reinforcement Learning, University of Luebeck},
pubstate = {published},
tppubtype = {inproceedings}
}
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