Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Francisco Roldan Sanchez"'
Publikováno v:
2022 Sixth IEEE International Conference on Robotic Computing (IRC).
Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world applications. This paper explores Hindsight Experience Replay both in sim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b75e92936da06051b3c01482c46bd480
https://doi.org/10.1109/irc55401.2022.00058
https://doi.org/10.1109/irc55401.2022.00058
Autor:
Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaier, Stefan Bauer, Stephen J. Redmond
Publikováno v:
Expert Systems.
This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult manipulation task. The RRC consisted of using a TriFinger robot to manipulate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d71c21b80e6a9ae1dd3aa60115538274
https://doi.org/10.1111/exsy.13205
https://doi.org/10.1111/exsy.13205
Autor:
Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Stephen J. Redmond, Noel E. O'Connor
End-to-end reinforcement learning techniques are among the most successful methods for robotic manipulation tasks. However, the training time required to find a good policy capable of solving complex tasks is prohibitively large. Therefore, depending
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e030de35fc43675a8e735c9f3a9e89d0
