GeneSIS-Rt: Generating Synthetic Images for Training Secondary Real-World Tasks
Name
1710.04280.pdf
Description
Accepted version
Size
2.92 MB
Format
Adobe PDF
Checksum (MD5)
aeb9e9000fabf75bfe2a003d862bbb97
Author(s)
Stein, Gregory Joseph
Roy, Nicholas
Date Issued
September 2018
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Stein, Gregory J. and Nicholas Roy. "GeneSIS-Rt: Generating Synthetic Images for Training Secondary Real-World Tasks, IEEE International Conference on Robotics and Automation (ICRA), May 2018, Brisbane, QLD, Australia, Institute of Electrical and Electronics Engineers, September 2018. © 2018 IEEE
Version
Original manuscript
Abstract
We propose a novel approach for generating high-quality, synthetic data for domain-specific learning tasks, for which training data may not be readily available. We leverage recent progress in image-to-image translation to bridge the gap between simulated and real images, allowing us to generate realistic training data for real-world tasks using only unlabeled real-world images and a simulation. GeneSIS-Rtameliorates the burden of having to collect labeled real-world images and is a promising candidate for generating high-quality, domain-specific, synthetic data. To show the effectiveness of using GeneSIS-Rtto create training data, we study two tasks: semantic segmentation and reactive obstacle avoidance. We demonstrate that learning algorithms trained using data generated by GeneSIS-RT make high-accuracy predictions and outperform systems trained on raw simulated data alone, and as well or better than those trained on real data. Finally, we use our data to train a quadcopter to fly 60 meters at speeds up to 3.4 m/s through a cluttered environment, demonstrating that our GeneSIS-RT images can be used to learn to perform mission-critical tasks.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8462971
