# DexHub and DART: Towards Internet Scale Robot Data Collection

arXiv: https://arxiv.org/abs/2411.02214 · HTML: https://mlsanity.com/paper/2411.02214/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2024.

## Built on by (most cited first, 12 of 12)

- [EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video](https://mlsanity.com/paper/2505.11709/) (2025)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization](https://mlsanity.com/paper/2502.20382/) (2025)
- [DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation](https://mlsanity.com/paper/2509.04441/) (2025)
- [ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory](https://mlsanity.com/paper/2509.05314/) (2025)
- [Tune to Learn: How Controller Gains Shape Robot Policy Learning](https://mlsanity.com/paper/2604.02523/) (2026)
- [D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI](https://mlsanity.com/paper/2510.05684/) (2025)
- [Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation](https://mlsanity.com/paper/2605.00244/) (2026)
- [IRIS: An Immersive Robot Interaction System](https://mlsanity.com/paper/2502.03297/) (2025)
- [dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale](https://mlsanity.com/paper/2503.05646/) (2025)
- [COBALT: Crowdsourcing Robot Learning via Cloud-Based Teleoperation with Smartphones](https://mlsanity.com/paper/2605.19138/) (2026)
- [Pre-training Visual Dexterity in Simulation](https://mlsanity.com/paper/2608.15917/) (2026)

## Abstract (arXiv)

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