Title: Deep Liquid: Combining Liquid Simulation and Perception Using Deep Neural Networks
Advisor: Dieter Fox
Supervisory Committee: Dieter Fox (Chair), Samuel Burden (GSR, EE), Maya Cakmak, and Sidd Srinivasa
Abstract: Liquids are an essential part of many common tasks. While humans seem to be able to master them to varying degrees from a young age, this is still a challenge for robots. Here I propose a system to enable robots to handle liquids. I propose a Bayes' filter for liquids that combines deep neural networks with liquid simulation. The deep networks provide the dynamics and observation models of the filter while the simulator provides the training data. This combines both the generality of the simulator with the adaptability of the deep networks. The proposed method has the potential to make a significant contribution to the body of robotics research.