The quest to build a generalist robotic system is impeded by the scarcity of diverse and high-quality data. While real-world data collection effort exist, requirements for robot hardware, physical environment setups, and frequent resets significantly impede the scalability needed for modern learning frameworks. We introduce DART, a teleoperation platform designed for crowdsourcing that reimagines robotic data collection by leveraging cloud-based simulation and augmented reality (AR) to address many limitations of prior data collection efforts. Our user studies highlight that DART enables higher data collection throughput and lower physical fatigue compared to real-world teleoperation. We also demonstrate that policies trained using DART-collected datasets successfully transfer to reality and are robust to unseen visual disturbances. All data collected through DART is automatically stored in our cloud-hosted database, DexHub, which will be made publicly available upon curation, paving the path for DexHub to become an ever-growing data hub for robot learning. Videos are available at: https://dexhub.ai/project

翻译：构建通用机器人系统的追求因缺乏多样化高质量数据而受阻。尽管存在现实世界的数据收集工作，但对机器人硬件、物理环境设置以及频繁重置的要求严重阻碍了现代学习框架所需的可扩展性。我们推出DART——一个专为众包设计的遥操作平台，该平台通过利用云端仿真和增强现实（AR）技术重构机器人数据收集范式，以解决先前数据收集工作中的诸多局限。我们的用户研究表明，相较于现实世界遥操作，DART能实现更高的数据收集吞吐量和更低的生理疲劳度。我们还证明，使用DART收集数据集训练的策略能够成功迁移至现实场景，并对未见过的视觉干扰具有鲁棒性。所有通过DART收集的数据将自动存储于我们云端托管的数据库DexHub中，该数据库经整理后将向公众开放，为DexHub成为持续增长的机器人学习数据枢纽铺平道路。演示视频详见：https://dexhub.ai/project