Noise and the reality gap: The use of simulation in evolutionary robotics
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Extensive reliance on simulation and offline training further distances learning from the full variability and constraint structure of real sensorimotor interaction.
“Similarly, extensive reliance on simulation and offline training further distances learning from the full variability and constraint structure of real sensorimotor interaction (Jakobi et al., 1995; Cully et al., 2015).”
From Toward Enactive Artificial Intelligence (2026) · cites this paper as Jakobi et al., 1995
The reality gap (RG) is a mismatch of simulated and real control-law performances caused by inaccurate representation of the real environment in simulation, manifesting as degradation of a controller learnt in simulation when applied on the real world.
“a mismatch of simulated and real control-law performances caused by the inaccurate representation of the real environment in simulation.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [9]
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Reinforcement learning in robotics: A survey
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“Indeed, several works faced the RG problem in other frameworks, such as Evolutionary Computation [9, 11, 12, 13, 14, 15, 16, 17] or Model Predictive Control [18, 19, 20, 21].”
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Toward Enactive Artificial Intelligence
Rafiee, B., & Sutton, R. (2026) · arXiv · doi:10.48550/arxiv.2605.24238
- Authors
- Jakobi, N., Husbands, P., & Harvey, I.
- Affiliations
- University of Sussex (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1995
- Pages
- 704-720
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1481659984
- Cited by
- 631
- References
- 17
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 37 · 2023: 32 · 2022: 38 · 2021: 47 · 2020: 66 · 2019: 40 · 2018: 28 · 2017: 30 · 2016: 17 · 2015: 25 · 2014: 16 · 2013: 17 · 2012: 20
- FWCI
- 9.21
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Keywords
- evolutionary robotics, robot simulation, sim-to-real gap, artificial evolution, Khepera robot, obstacle avoidance
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