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of 160
pro vyhledávání: '"Moll, Mark"'
Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodeled systems. However, RoboticReinforcement Learning currently lacks clea
Externí odkaz:
http://arxiv.org/abs/1909.09282
Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This p
Externí odkaz:
http://arxiv.org/abs/1711.09604
Sampling-based planning algorithms are the most common probabilistically complete algorithms and are widely used on many robot platforms. Within this class of algorithms, many variants have been proposed over the last 20 years, yet there is still no
Externí odkaz:
http://arxiv.org/abs/1412.6673
We present an experienced-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces th
Externí odkaz:
http://arxiv.org/abs/1410.1950
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2006 Jun . 103(26), 9885-9890.
Externí odkaz:
https://www.jstor.org/stable/30051010
Publikováno v:
Assembly Automation, 2002, Vol. 22, Issue 1, pp. 46-54.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/01445150210416673
Akademický článek
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Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodeled systems. However, RoboticReinforcement Learning currently lacks clea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8865e5ac6d7a6af1f2b4d306a22323f
http://arxiv.org/abs/1909.09282
http://arxiv.org/abs/1909.09282
Autor:
Hernández Vega, Juan David, Vidal Garcia, Eduard, Moll, Mark, Palomeras Rovira, Narcís, Carreras Pérez, Marc, Kavraki, Lydia E.
Publikováno v:
Journal Of Field Robotics (1556-4959) (Wiley), 2019-03, Vol. 36, N. 2, P. 370-396
© Journal of Field Robotics, 2019, vol. 36, núm. 2, p. 370-396
Articles publicats (D-ATC)
DUGiDocs – Universitat de Girona
instname
© Journal of Field Robotics, 2019, vol. 36, núm. 2, p. 370-396
Articles publicats (D-ATC)
DUGiDocs – Universitat de Girona
instname
We present an approach to endow an autonomous underwater vehicle with the capabilities to move through unexplored environments. To do so, we propose a computational framework for planning feasible and safe paths. The framework allows the vehicle to i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::12bc2285894b86fc475593d4a5005122
https://archimer.ifremer.fr/doc/00668/78007/
https://archimer.ifremer.fr/doc/00668/78007/
Publikováno v:
BMC Genomics. 8/18/2016, Vol. 17, p327-339. 13p. 3 Diagrams, 6 Charts, 4 Graphs.