Deep Forward and Inverse Perceptual Models for Tracking and Prediction
Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots
Abstract
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.
BibTeX
@inproceedings{icra2018_deepforwardandin,
title = {Deep Forward and Inverse Perceptual Models for Tracking and Prediction},
author = {Alexander Lambert and Amirreza Shaban and Amit Raj and Zhen Liu and Byron Boots},
booktitle = {ICRA 2018},
year = {2018}
}