论文标题
Tartandrive：用于学习越野动态模型的大型数据集
TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models
论文作者
论文摘要
我们提出了Tartandrive，这是一个用于学习越野驾驶动态模型的大型数据集。我们收集了一个大约200,000个越野驾驶互动的数据集，对经过改进的Yamaha Viking ATV，具有七种独特的传感方式。据作者所知，这是最大的现实世界多模式越野驾驶数据集，无论是在互动数量和传感方式方面。我们还基准了几种从该数据集上的高维观测值中基于模型的增强学习学习的最新方法。我们发现，将这些模型扩展到多模式性会导致越野动态预测的显着性能，尤其是在更具挑战性的地形上。我们还确定了当前的神经网络体系结构的一些缺点，以完成越野驾驶任务。我们的数据集可从https://github.com/castacks/tartan_drive获得。
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.
