DART: Dexterous Augmented Reality Teleoperation Platform for Large-Scale Robot Data Collection in Simulation

Younghyo Park, Jagdeep Singh Bhatia, Lars Ankile, Pulkit Agrawal

Abstract: The scarcity of diverse and high-quality data impedes the quest to build a generalist robotic system. Current robotics data collection efforts face many challenges: the need for physical robotic hardware, setting up the environment, frequent resets, and the fatigue for data collectors operating real robots. 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. User studies show that DART enables higher data collection throughput and lower physical fatigue than 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 a cloud-hosted database, DexHub, paving the path for an ever-growing data hub for robot learning. https://dexhub.ai/project

External IDs:dblp:conf/icra/ParkBAA25