# A Survey of Deep Learning Techniques for Mobile Robot Applications

arXiv: https://arxiv.org/abs/1803.07608 · HTML: https://mlsanity.com/paper/1803.07608/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast](https://mlsanity.com/paper/2106.03796/) (2021)
- [Enabling On-Device Learning via Experience Replay with Efficient Dataset Condensation](https://mlsanity.com/paper/2405.16113/) (2024)
- [A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments](https://mlsanity.com/paper/2408.00997/) (2024)

## 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.