TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
Samuel Triest, Matthew Sivaprakasam, Sean J. Wang, Wenshan Wang, Aaron M. Johnson, Sebastian Scherer
Abstract
We present TartanDrive, a large scale dataset for learning dynamics models for off-road driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modified Yamaha Viking ATV with seven unique sensing modalities in diverse terrains. To the authors' knowledge, this is the largest real-world multi-modal off-road driving dataset, both in terms of number of interactions and sensing modalities. We also benchmark several state-of-the-art methods for model-based reinforcement learning from high-dimensional observations on this dataset. We find that extending these models to multi-modality leads to significant performance on off-road dynamics prediction, especially in more challenging terrains. We also identify some shortcomings with current neural network architectures for the off-road driving task. Our dataset is available at https://github.com/castacks/tartan_drive.
BibTeX
@inproceedings{icra2022_tartandrivealarg,
title = {TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models},
author = {Samuel Triest and Matthew Sivaprakasam and Sean J. Wang and Wenshan Wang and Aaron M. Johnson and Sebastian Scherer},
booktitle = {ICRA 2022},
year = {2022}
}