Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not

By Lei Tai. 2016

78 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “There are many research efforts that have benefited from DL to develop an intelligent control software for mobile robots [228], [229].”
Deep reinforcement learning with successor features for navigation across similar environments (2016, methodology): “More recently, there have been several attempts at combining task transfer with Deep RL [19], [20], [21], [22], [23], [24].”
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation (2017, methodology): “Reinforcement learning has been widely applied in robotic tasks [16], [17].”
Synthetic Data for Deep Learning (2019, background): “1, simulated environments are almost inevitable in reinforcement learning for robotics, as they allow to scale the datasets up compared to real data and cover a much wider range of situations than real data that could be used for imitational learning (see also the survey [574]).”
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles (2017, background): “An evidence of the suitability of deep learning for many kinds of autonomous robotic applications is the increasing trend in deep learning robot related scientific publications over the past decades, which is expected to continue growing [8].”