Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Alexander Fabisch"'
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 17 (2020)
Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often
Externí odkaz:
https://doaj.org/article/f4f881d2e14f4398b5199fce3620eb26
Autor:
Raul Dominguez, Mark Post, Alexander Fabisch, Romain Michalec, Vincent Bissonnette, Shashank Govindaraj
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 17 (2020)
Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Fra
Externí odkaz:
https://doaj.org/article/4a93cccd5d1140548db9e38afa19d0be
Autor:
Lisa Gutzeit, Alexander Fabisch, Marc Otto, Jan Hendrik Metzen, Jonas Hansen, Frank Kirchner, Elsa Andrea Kirchner
Publikováno v:
Frontiers in Robotics and AI, Vol 5 (2018)
We describe the BesMan learning platform which allows learning robotic manipulation behavior. It is a stand-alone solution which can be combined with different robotic systems and applications. Behavior that is adaptive to task changes and different
Externí odkaz:
https://doaj.org/article/8e0a6d45bb934793adf231fdf5e1085e
Publikováno v:
KI 2019: Advances in Artificial Intelligence-42nd German Conference on AI, Kassel, Germany, September 23–26, 2019, Proceedings
KI 2019: Advances in Artificial Intelligence ISBN: 9783030301781
KI
Lecture Notes in Computer Science
Lecture Notes in Computer Science-KI 2019: Advances in Artificial Intelligence
KI 2019: Advances in Artificial Intelligence ISBN: 9783030301781
KI
Lecture Notes in Computer Science
Lecture Notes in Computer Science-KI 2019: Advances in Artificial Intelligence
Transferring human movements to robotic systems is of high interest to equip the systems with new behaviors without expert knowledge. Typically, skills are often only learned for a very specific setup and a certain robot. We propose a modular framewo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b001afc88dc40d0a56e993a96f59b976
Autor:
Alexander Fabisch
Publikováno v:
Journal of Open Source Software. 6:3054
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5b997a249b51b4cb0114b7f304c6cbd
https://doi.org/10.21105/joss.03054
https://doi.org/10.21105/joss.03054
Autor:
Alexander Fabisch, Raúl Domínguez, Vincent Bissonnette, Mark Post, Shashank Govindaraj, Romain Michalec
Publikováno v:
International Journal of Advanced Robotic Systems, Vol 17 (2020)
International Journal of Advanced Robotic Systems
International Journal of Advanced Robotic Systems
Multisensor data fusion plays a vital role in providing autonomous systems with environmental information crucial for reliable functioning. In this article, we summarize the modular structure of the newly developed and released Common Data Fusion Fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::426214a8235f219f14b5f346051a9739
https://doi.org/10.1177/1729881420911767
https://doi.org/10.1177/1729881420911767
Publikováno v:
KI - Künstliche Intelligenz. 29:369-377
Contextual policy search is a reinforcement learning approach for multi-task learning in the context of robot control learning. It can be used to learn versatilely applicable skills that generalize over a range of tasks specified by a context vector.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::835f36af7a9adb38f47925ac83dad6ad
https://doi.org/10.1007/s13218-015-0363-2
https://doi.org/10.1007/s13218-015-0363-2
Publikováno v:
IROS
This paper presents an online technique which employs incremental support vector regression to learn the damping term of an underwater vehicle motion model, subject to dynamical changes in the vehicle's body. To learn the damping term, we use data co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8a86baf937bd4884c6c2e2c1abac319
https://doi.org/10.1109/iros.2017.8206278
https://doi.org/10.1109/iros.2017.8206278
Publikováno v:
Neural Networks. 42:83-93
We examine two methods which are used to deal with complex machine learning problems: compressed sensing and model compression. We discuss both methods in the context of feed-forward artificial neural networks and develop the backpropagation method i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d4867e26cdd42e87ca97399a4e56193
https://doi.org/10.1016/j.neunet.2013.01.020
https://doi.org/10.1016/j.neunet.2013.01.020
Autor:
Jan Hendrik Metzen, José de Gea Fernández, Elsa Andrea Kirchner, Alexander Fabisch, Lisa Senger
Learning versatile, reusable skills is one of the key prerequisites for autonomous robots. Imitation and reinforcement learning are among the most prominent approaches for learning basic robotic skills. However, the learned skills are often very spec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05b437a91e440d52093dfa166bd382ed
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85087961578
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85087961578
