TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

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TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models

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.

Citation

Please read our paper for details.

@inproceedings{triest2022tartandrive,
  title={Tartandrive: A large-scale dataset for learning off-road dynamics models},
  author={Triest, Samuel and Sivaprakasam, Matthew and Wang, Sean J and Wang, Wenshan and Johnson, Aaron M and Scherer, Sebastian},
  booktitle={2022 International Conference on Robotics and Automation (ICRA)},
  pages={2546--2552},
  year={2022},
  organization={IEEE}
}

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Contact

Wenshan Wang - (wenshanw [at] andrew [dot] cmu [dot] edu)

Sebastian Scherer - (basti [at] cmu [dot] edu)

Term of use

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.