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Robot Learning From Randomized Simulations: A Review

Muratore, Fabio ; Ramos, Fabio ; Turk, Greg ; Yu, Wenhao ; Gienger, Michael ; Peters, Jan (2022):
Robot Learning From Randomized Simulations: A Review. (Publisher's Version)
In: Frontiers in Robotics and AI, 9, Frontiers, e-ISSN 2296-9144,
DOI: 10.26083/tuprints-00021227,

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Item Type: Article
Origin: Secondary publication via sponsored Golden Open Access
Status: Publisher's Version
Title: Robot Learning From Randomized Simulations: A Review
Language: English

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 generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based on models, hence inevitably imperfect. This raises the question of how simulators can be modified to facilitate learning robot control policies and overcome the mismatch between simulation and reality, often called the “reality gap.” We provide a comprehensive review of sim-to-real research for robotics, focusing on a technique named “domain randomization” which is a method for learning from randomized simulations.

Journal or Publication Title: Frontiers in Robotics and AI
Volume of the journal: 9
Place of Publication: Darmstadt
Publisher: Frontiers
Collation: 19 Seiten
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Divisions: 20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 04 May 2022 13:47
Last Modified: 23 Aug 2022 09:41
DOI: 10.26083/tuprints-00021227
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-212275
Additional Information:

Keywords: robotics, simulation, reality gap, simulation optimization bias, reinforcement learning, domain randomization, sim-to-real

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21227
PPN: 494561076
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