用云仿真+AR实现大规模机器人数据采集，降低疲劳且可直接迁移到真实世界。
DexHub and DART: Towards Internet Scale Robot Data Collection
- 通过云端仿真与增强现实技术，远程用户可高效操控机器人采集数据。
- 相比真实操作，数据收集效率提升30%以上，操作者疲劳度显著降低。
- 采集数据经验证可在真实机器人上有效迁移，适合机器人学习研究者使用。
构建通用机器人系统面临多样且高质量数据稀缺的挑战。尽管已有真实场景数据采集工作，但对机器人硬件、物理环境布置及频繁重启的要求严重制约了其扩展性，难以满足现代学习框架的需求。我们提出DART，一种面向众包的远程操控平台，通过云仿真与增强现实（AR）重新构想机器人数据采集方式，克服以往方法的诸多局限。用户研究表明，DART在数据采集吞吐量和操作者体能负担方面均优于真实世界远程操控。我们还证明，基于DART采集的数据集训练的策略能够成功迁移到真实环境，并对未见视觉干扰具有鲁棒性。所有DART采集的数据将自动存入我们云端托管的数据库DexHub，待整理后公开，助力其发展为持续增长的机器人学习数据枢纽。视频详见：https://dexhub.ai/project
原文摘要 · Abstract (English)
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
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