Muratore, Fabio ; Ramos, Fabio ; Turk, Greg ; Yu, Wenhao ; Gienger, Michael ; Peters, Jan (2022)
Robot Learning From Randomized Simulations: A Review.
In: Frontiers in Robotics and AI, 9
doi: 10.3389/frobt.2022.799893
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Muratore, Fabio ; Ramos, Fabio ; Turk, Greg ; Yu, Wenhao ; Gienger, Michael ; Peters, Jan |
| Art des Eintrags: | Bibliographie |
| Titel: | Robot Learning From Randomized Simulations: A Review |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Verlag: | Frontiers |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Robotics and AI |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| Kollation: | 19 Seiten |
| DOI: | 10.3389/frobt.2022.799893 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Zusätzliche Informationen: | Keywords: robotics, simulation, reality gap, simulation optimization bias, reinforcement learning, domain randomization, sim-to-real |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Intelligente Autonome Systeme |
| Hinterlegungsdatum: | 02 Aug 2024 12:40 |
| Letzte Änderung: | 02 Aug 2024 12:40 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Robot Learning From Randomized Simulations: A Review. (deposited 04 Mai 2022 13:47)
- Robot Learning From Randomized Simulations: A Review. (deposited 02 Aug 2024 12:40) [Gegenwärtig angezeigt]
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