Deep learning in robotics: a review of recent research
By Harry A. Pierson, Michael S. Gashler. Adv. Robotics, 2017
299 citations recorded by the source corpus.
Abstract 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.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/b3d7a7440bb170a935589f6e12aa665fb7516b47
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In Computer Vision and Pattern Recognition
- Justin A. Sirignano
- Konstantinos Spiliopoulos
- Stan Z. Li
- Sheng Jin
- P. Heng
- Shijian Lu
- Zhangyang Gao
- Jingyi Zhang
In Control and Systems Engineering
- Humaira Arshad
- Aman Bin Jantan
- Justin A. Sirignano
- Konstantinos Spiliopoulos
- Oludare Isaac Abiodun
- Abiodun Esther Omolara
- Kemi Victoria Dada
Citations in the corpus
- State-of-the-art in artificial neural network applications: A survey (2018, methodology): “Furthermore, ANNs or deep learning techniques has been applying to human-robot interaction systems that yielded results [116, 117, 118].”
- Vision-Language Models for Vision Tasks: A Survey (2023, background): “Visual recognition ( e.g. , image classification, object detection and semantic segmentation) is a long-standing challenge in computer vision research, and it is also the cornerstone of a myriad of computer vision applications in autonomous driving [1], remote sensing [2], robotics [3], etc.”
- Mean Field Analysis of Deep Neural Networks (2019, background): “Neural networks also find increasing more applications in engineering, robotics, medicine, and finance (see [21], [22], [43], [15], [32], [1], [36], [37], [38], and [39]).”
- A Survey on Generative Diffusion Models (2022, background): “However, certain data in fields like robotics [130], geoscience [131], and protein modeling [132] are definedwithinaRiemannianmanifold[133].”
- Mean Field Analysis of Neural Networks: A Law of Large Numbers (2018, background): “[51], [22], and [40] describe applications in robotics.”