Robot Learning From Randomized Simulations: A Review

The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data gener...

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Veröffentlicht in:	Frontiers in robotics and AI Jg. 9; S. 799893
Hauptverfasser:	Muratore, Fabio, Ramos, Fabio, Turk, Greg, Yu, Wenhao, Gienger, Michael, Peters, Jan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media S.A 11.04.2022
Schlagworte:	domain randomization reality gap reinforcement learning robotics Robotics and AI simulation simulation optimization bias simulation optimization bias reality gap simulation robotics sim-to-real domain randomization reinforcement learning
ISSN:	2296-9144, 2296-9144
Online-Zugang:	Volltext
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