A Survey of Deep Learning Techniques for Mobile Robot Applications
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Abstract (arXiv)
Advancements in deep learning over the years have attracted research into how deep artificial neural networks can be used in robotic systems. This research survey will present a summarization of the current research with a specific focus on the gains and obstacles for deep learning to be applied to mobile robotics.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.
- 2021Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrastcited by 21
- 2024Enabling On-Device Learning via Experience Replay with Efficient Dataset Condensationcited by 0
- 2024A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environmentscited by 0
- 2024A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments2 Aug 2024
- 2024Enabling On-Device Learning via Experience Replay with Efficient Dataset Condensation25 May 2024
- 2021Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast7 Jun 2021