A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
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Modern autonomous systems mostly use deep learning models to accomplish tasks.
“For the most part, modern autonomous systems use deep learning models to accomplish this [11].”
From Autonomous Task Completion Based on Goal-directed Answer Set Programming (2025) · cites this paper as [11]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037 · see citing works
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“An alternative approach is to use a self-play scheme for learning a complex task [157]; it is in a way, a curriculum learning [10] approach where the tasks to learn are dynamically defined.”
MethodFlownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313 · see citing works
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160 · see citing works
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202133 · Cited by 3,124 · see citing works
The YCB object and model set: Towards common benchmarks for manipulation research
Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., & Dollar, A. M. (2015) · 2015 International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2015.7251504 · Cited by 838 · see citing works
Self-Supervised GANs via Auxiliary Rotation Loss
Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01243 · Cited by 328 · see citing works
A Survey on Bayesian Deep Learning
Wang, H., & Yeung, D.-Y. (2021) · ACM Computing Surveys · doi:10.1145/3409383 · Cited by 247 · see citing works
VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control
Zhang, J., Tai, L., Yun, P., Xiong, Y., Liu, M., Boedecker, J., & Burgard, W. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2894216 · Cited by 109 · cited 6× · see citing works
“Reference [16,27,28,42,49, 107,108,163] [13,21,31,75,79, 99] [2,12,21,30,86, 116,158,172, 193] [39,60,77,85, 118,121,179] [84,124,133,152, 192,194]”
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Autonomous Task Completion Based on Goal-directed Answer Set Programming
Tudor, A. R. (2025) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.416.39
- Authors
- Morales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A.
- Affiliations
- Mathematics Research Center (MX)
- Corresponding author
- Becerra, Israel
- Venue
- Intelligent Service Robotics
- Published
- 2021-11
- Volume
- 14
- Issue
- 5
- Pages
- 773-805
- ISSN
- 1861-2776 (ISSN-L)
- 1861-2784
- OpenAlex
- W3211352205
- Cited by
- 101
- References
- 11
- Citations by year
- 2026: 8 · 2025: 34 · 2024: 35 · 2023: 18 · 2022: 6
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Intelligent Service Robotics (publishedVersion)
- Funders
- Consejo Nacional de Ciencia y Tecnología (http://dx.doi.org/10.13039/501100003141)
- 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 › Robotics and Automated Systems
- Keywords
- deep reinforcement learning, robot policy learning, sensor noise robustness
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