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.
@article{arxiv.1710.11311,
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},
journal= {arXiv preprint arXiv:1710.11311},
year = {2018}
}
Comments
8 pages, International Conference on Robotics and Automation (ICRA) 2018