A deep-network solution towards model-less obstacle avoidance

By Lei Tai, Shaohua Li, Ming Liu. 2016

215 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, positive): “Tai et al. (2016) drive an indoor mobile robot autonomously through a dataset based on joystick commands from human demonstrator.”
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies (2016, unknown): “Especially robotic vision tasks – robotic tasks based directly on real image data – such as, navigation [Tai et al. , 2016], object grasping and manipulation [Levine et al. , 2016b; Pinto and Gupta, 2016; Lenz et al. , 2015] have seen increased interest.”
Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks (2017, background): “tasks – robotic tasks based directly on real image data – such as navigation [12], object grasping and manipulation [3], [11], [13] have seen increased interest.”
Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation (2017, methodology): “For learning-based obstacle avoidance, deep neural networks have been successfully applied on monocular images [11] and depth images [12].”
Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “Raw sensor information (depth image from an RGB-D sensor) was taken as input to connect with the robot control command directly (Tai et al., 2016).”
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots (2016, methodology): “The test results are compared with the exploration strategies separately based on deep learning [1] or reinforcement learning [3].”
Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning (2017, methodology): “Also, with the improvement of the computation power of mobile platforms, such approaches have been utilized in various robotics tasks such as obstacle avoidance [4].”