Deep learning in robotics: A review of recent research
Open accessAbstract
Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least 30 papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.
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The main limitations preventing deep learning in medical robotics are the huge volume of training data required and a relatively long training time.
“they found that the main limitations preventing deep learning in medical robotics are the huge volume of training data required and a relatively long training time.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [65]
Cites
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What is the best multi-stage architecture for object recognition?
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“Levine et al. [128] used reinforcement learning (Structure D) to show that this can be superior to separate systems for perception and control.”
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Vision-Language Models for Vision Tasks: A Survey
Zhang, J., Huang, J., Jin, S., & Lu, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3369699 · Cited by 1,051 · see citing works
Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228 · see citing works
A bird's-eye view of deep learning in bioimage analysis
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System Design Perspective for Human-Level Agents Using Deep Reinforcement Learning: A Survey
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Human-inspired Perspectives: A Survey on AI Long-term Memory
He, Z., Lin, W., Zheng, H., Zhang, F., Jones, M. W., Aitchison, L., Xu, X., Liu, M., Kristensson, P. O., & Shen, J. (2024) · arXiv · doi:10.48550/arxiv.2411.00489 · Cited by 3 · see citing works
“Beyond the two applications aforementioned, AI long-term memory can also enhance various domains such as content personalization (e.g. recommender systems [325, 252, 326]) and motion control (e.g. robotics [327, 328, 329]).”
- Authors
- Pierson, H. A., & Gashler, M. S.
- Affiliations
- University of Arkansas at Fayetteville (US)
- Corresponding author
- Pierson, Harry A.
- Venue
- Advanced Robotics
- Publisher
- Informa UK Limited
- Published
- 2017-08-18
- Volume
- 31
- Issue
- 16
- Pages
- 821-835
- ISSN
- 0169-1864 (ISSN-L)
- 1568-5535
- OpenAlex
- W2736506089
- Cited by
- 331
- References
- 7
- Citations by year
- 2026: 18 · 2025: 51 · 2024: 35 · 2023: 60 · 2022: 50 · 2021: 58 · 2020: 35 · 2019: 14 · 2018: 9 · 2017: 1
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Advanced Robotics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- deep learning, robotics, deep neural network
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