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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| Vydáno v: | Frontiers in robotics and AI Ročník 9; s. 799893 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
Frontiers Media S.A
11.04.2022
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| ISSN: | 2296-9144, 2296-9144 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Gienger, Michael Turk, Greg Ramos, Fabio Yu, Wenhao Peters, Jan Muratore, Fabio |
| AuthorAffiliation | 6 Robotics at Google , Mountain View , CA , United States 1 Intelligent Autonomous Systems Group , Technical University of Darmstadt , Darmstadt , Germany 3 School of Computer Science , University of Sydney , Sydney , NSW , Australia 4 NVIDIA , Seattle , WA , United States 5 Georgia Institute of Technology , Atlanta , GA , United States 2 Honda Research Institute Europe , Offenbach am Main , Germany |
| AuthorAffiliation_xml | – name: 6 Robotics at Google , Mountain View , CA , United States – name: 2 Honda Research Institute Europe , Offenbach am Main , Germany – name: 3 School of Computer Science , University of Sydney , Sydney , NSW , Australia – name: 4 NVIDIA , Seattle , WA , United States – name: 5 Georgia Institute of Technology , Atlanta , GA , United States – name: 1 Intelligent Autonomous Systems Group , Technical University of Darmstadt , Darmstadt , Germany |
| Author_xml | – sequence: 1 givenname: Fabio surname: Muratore fullname: Muratore, Fabio – sequence: 2 givenname: Fabio surname: Ramos fullname: Ramos, Fabio – sequence: 3 givenname: Greg surname: Turk fullname: Turk, Greg – sequence: 4 givenname: Wenhao surname: Yu fullname: Yu, Wenhao – sequence: 5 givenname: Michael surname: Gienger fullname: Gienger, Michael – sequence: 6 givenname: Jan surname: Peters fullname: Peters, Jan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35494543$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2022 Muratore, Ramos, Turk, Yu, Gienger and Peters. Copyright © 2022 Muratore, Ramos, Turk, Yu, Gienger and Peters. 2022 Muratore, Ramos, Turk, Yu, Gienger and Peters |
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| Keywords | simulation optimization bias reality gap simulation robotics sim-to-real domain randomization reinforcement learning |
| Language | English |
| License | Copyright © 2022 Muratore, Ramos, Turk, Yu, Gienger and Peters. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 Konstantinos Chatzilygeroudis, University of Patras, Greece Reviewed by: Akansel Cosgun, Monash University, Australia This article was submitted to Robot Learning and Evolution, a section of the journal Frontiers in Robotics and AI Edited by: Antonio Fernández-Caballero, University of Castilla-La Mancha, Spain |
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| Title | Robot Learning From Randomized Simulations: A Review |
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