@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},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/yddDZ7z9oqxenCi},

doi = {10.3390/app12063153},

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.},

keywords = {autonomous navigation, computer vision, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{Vecchio2021,

title = {Peripheral Neuroergonomics – An Elegant Way to Improve Human-Robot Interaction?},

author = {Del Vecchio, Alessandro and Castellini, Claudio and Beckerle, Philipp},

doi = {10.3389/fnbot.2021.691508},

issn = {1662-5218},

year  = {2021},

date = {2021-09-16},

journal = {Frontiers in Neurorobotics},

volume = {15},

pages = {117},

keywords = {Human-Machine Interfaces, Human-Robot-Interaction, University of Erlangen},

pubstate = {published},

tppubtype = {article}

}

@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},

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}

}

@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://cps.unileoben.ac.at/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}

}

@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},

keywords = {Human Motor Control, Movement Primitives, Reinforcement Learning, University of Erlangen, University of Luebeck},

pubstate = {published},

tppubtype = {article}

}