Sim2Real in Robotics and Automation: Applications and Challenges
Open accessAbstract
To Perform reliably and consistently over sustained periods of time, large-scale automation critically relies on computer simulation. Simulation allows us and supervisory AI to effectively design, validate, and continuously improve complex processes, and helps practitioners to gain insight into the operation and justify future investments. While numerous successful applications of simulation in industry exist, such as circuit simulation, finite element methods, and computeraided design (CAD), state-of-the-art simulators fall short of accurately modeling physical phenomena, such as friction, impact, and deformation.
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Any technique in which the controller design relies on a simulator of the real system can potentially exhibit a reality gap.
“Any technique in which the controller design relies on a simulator of the real system can potentially exhibit a reality gap [10].”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [10]
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References 3 · Cited by 154
- Authors
- Hofer, S., Bekris, K., Handa, A., Gamboa, J. C., Mozifian, M., Golemo, F., Atkeson, C., Fox, D., Goldberg, K., Leonard, J., Karen Liu, C., Peters, J., Song, S., Welinder, P., & White, M.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Nvidia (United States) (US)
- McGill University (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- University of Washington (US)
- University of California, Berkeley (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Technische Universität Darmstadt (DE)
- Columbia University (US)
- OpenAI (United States) (US)
- University of Alberta (CA)
- ORCID
- Venue
- IEEE Transactions on Automation Science and Engineering
- Published
- 2021-04
- Volume
- 18
- Issue
- 2
- Pages
- 398-400
- ISSN
- 1545-5955 (ISSN-L)
- 1558-3783
- OpenAlex
- W3152331843
- Cited by
- 154
- References
- 3
- Citations by year
- 2026: 28 · 2025: 50 · 2024: 35 · 2023: 30 · 2022: 8 · 2021: 3
- FWCI
- 6.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Transactions on Automation Science and Engineering (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Keywords
- sim-to-real transfer, physical simulation, friction modeling, impact modeling, deformation modeling, industrial robotics, simulation accuracy
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