Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning
The growing demand for robots able to act autonomously in complex scenarios has widely accelerated the introduction of Reinforcement Learning (RL) in robots control applications. However, the <italic>trial and error</italic> intrinsic nature of RL may result in long training time on real...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/9f38aed8c13146a8bbfadadf1817d86b |
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| Sumario: | The growing demand for robots able to act autonomously in complex scenarios has widely accelerated the introduction of Reinforcement Learning (RL) in robots control applications. However, the <italic>trial and error</italic> intrinsic nature of RL may result in long training time on real robots and, moreover, it may lead to dangerous outcomes. While simulators are useful tools to accelerate RL training and to ensure safety, they often are provided only with an approximated model of robot dynamics and of its interaction with the surrounding environment, thus resulting in what is called the <italic>reality gap</italic> (RG): a mismatch of simulated and real control-law performances caused by the inaccurate representation of the real environment in simulation. The most undesirable result occurs when the controller learnt in simulation fails the task on the real robot, thus resulting in an unsuccessful <italic>sim-to-real</italic> transfer. The goal of the present survey is threefold: (1) to identify the main approaches to face the RG problem in the context of robot control with RL, (2) to point out their shortcomings, and (3) to outline new potential research areas. |
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