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, 2022, 9
doi: 10.26083/tuprints-00021227
Artikel, Zweitveröffentlichung, Verlagsversion
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: | Zweitveröffentlichung |
| Titel: | Robot Learning From Randomized Simulations: A Review |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Frontiers |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Robotics and AI |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| Kollation: | 19 Seiten |
| DOI: | 10.26083/tuprints-00021227 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21227 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| 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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-212275 |
| 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: | 04 Mai 2022 13:47 |
| Letzte Änderung: | 27 Okt 2023 10:16 |
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