62 citations · 122 across the 6 of their papers we have counts for
6 papers · 1 filter
Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics
Grady Williams, Brian Goldfain, James M. Rehg +1
We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to au…
Vision-Based High Speed Driving with a Deep Dynamic Observer
Paul Drews, Grady Williams, Brian Goldfain +2
In this paper we present a framework for combining deep learning-based road detection, particle filters, and Model Predictive Control (MPC) to drive aggressively using only a monoc…
Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model
Paul Drews, Grady Williams, Brian Goldfain +2
We present a framework for vision-based model predictive control (MPC) for the task of aggressive, high-speed autonomous driving. Our approach uses deep convolutional neural networ…
Autonomous Racing with AutoRally Vehicles and Differential Games
Grady Williams, Brian Goldfain, Paul Drews +2
Safe autonomous vehicles must be able to predict and react to the drivers around them. Previous control methods rely heavily on pre-computation and are unable to react to dynamic e…
Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving
Grady Williams, Paul Drews, Brian Goldfain +2
We present an information theoretic approach to stochastic optimal control problems that can be used to derive general sampling based optimization schemes. This new mathematical me…
GPU Based Path Integral Control with Learned Dynamics
Grady Williams, Eric Rombokas, Tom Daniel
We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage…