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...

Celý popis

Podrobná bibliografia

Vydané v:	Frontiers in robotics and AI Ročník 9; s. 799893
Hlavní autori:	Muratore, Fabio, Ramos, Fabio, Turk, Greg, Yu, Wenhao, Gienger, Michael, Peters, Jan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media S.A 11.04.2022
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!