オフロード走行のダイナミクス学習のための大規模データセットTartanDrive
TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
多様な地形で7種類のセンサを用いて約20万回のオフロード走行データを収集し、モデルベース強化学習手法のベンチマークを行った大規模データセット論文。
著者: Samuel Triest, Matthew Sivaprakasam, Sean J. Wang, Wenshan Wang, Aaron M. Johnson, Sebastian Scherer
分類: cs.RO
原文アブストラクト
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.
