Practical Techniques for Using Neural Networks to Estimate State From Images

     

This paper is a part of our work on using neural networks to train robots to perform problems. It includes practical techniques for dealing with huge images, chaotic systems, and more. Abstract: An important task for training a robot (virtual or real) is to estimate state. State includes the state of the robot and its environment. Images from digital cameras are commonly used to monitor the robot due to the rich information, and low-cost hardware.


Master's Thesis, 2015

     


Fourier Networks 2016

     


Fourier Networks 2014

     


Forward Bipartite Alignment