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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.20808 |
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| _version_ | 1866909865981509632 |
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| author | Aljalbout, Elie Xing, Jiaxu Romero, Angel Akinola, Iretiayo Garrett, Caelan Reed Heiden, Eric Gupta, Abhishek Hermans, Tucker Narang, Yashraj Fox, Dieter Scaramuzza, Davide Ramos, Fabio |
| author_facet | Aljalbout, Elie Xing, Jiaxu Romero, Angel Akinola, Iretiayo Garrett, Caelan Reed Heiden, Eric Gupta, Abhishek Hermans, Tucker Narang, Yashraj Fox, Dieter Scaramuzza, Davide Ramos, Fabio |
| contents | Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driven by the extensive use of simulation as a critical tool for training and testing robotic systems prior to their deployment in real-world environments. However, simulations consist of abstractions and approximations that inevitably introduce discrepancies between simulated and real environments, known as the reality gap. These discrepancies significantly hinder the successful transfer of systems from simulation to the real world. Closing this gap remains one of the most pressing challenges in robotics. Recent advances in sim-to-real transfer have demonstrated promising results across various platforms, including locomotion, navigation, and manipulation. By leveraging techniques such as domain randomization, real-to-sim transfer, state and action abstractions, and sim-real co-training, many works have overcome the reality gap. However, challenges persist, and a deeper understanding of the reality gap's root causes and solutions is necessary. In this survey, we present a comprehensive overview of the sim-to-real landscape, highlighting the causes, solutions, and evaluation metrics for the reality gap and sim-to-real transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20808 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Reality Gap in Robotics: Challenges, Solutions, and Best Practices Aljalbout, Elie Xing, Jiaxu Romero, Angel Akinola, Iretiayo Garrett, Caelan Reed Heiden, Eric Gupta, Abhishek Hermans, Tucker Narang, Yashraj Fox, Dieter Scaramuzza, Davide Ramos, Fabio Robotics Artificial Intelligence Machine Learning I.2.6; I.2.8; I.2.9 Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driven by the extensive use of simulation as a critical tool for training and testing robotic systems prior to their deployment in real-world environments. However, simulations consist of abstractions and approximations that inevitably introduce discrepancies between simulated and real environments, known as the reality gap. These discrepancies significantly hinder the successful transfer of systems from simulation to the real world. Closing this gap remains one of the most pressing challenges in robotics. Recent advances in sim-to-real transfer have demonstrated promising results across various platforms, including locomotion, navigation, and manipulation. By leveraging techniques such as domain randomization, real-to-sim transfer, state and action abstractions, and sim-real co-training, many works have overcome the reality gap. However, challenges persist, and a deeper understanding of the reality gap's root causes and solutions is necessary. In this survey, we present a comprehensive overview of the sim-to-real landscape, highlighting the causes, solutions, and evaluation metrics for the reality gap and sim-to-real transfer. |
| title | The Reality Gap in Robotics: Challenges, Solutions, and Best Practices |
| topic | Robotics Artificial Intelligence Machine Learning I.2.6; I.2.8; I.2.9 |
| url | https://arxiv.org/abs/2510.20808 |