AutoDex自动搞定灵巧抓取数据收集，比遥操作快4.8倍，成功率碾压纯模拟验证
AutoDex是一个自动化真实世界数据收集系统，用于灵巧抓取。它利用20个摄像头在严重手-物遮挡下定位物体，执行碰撞监控的运动，标记抓取成功或失败，并主动重置物体。在100个不同物体上使用Allegro和Inspire手收集了3,593次抓取试验。与遥操作相比，处理500次轨迹只需10.3小时（遥操作49.4小时），吞吐量提升4.8倍。从AutoDex验证数据库检索的抓取成功率为76%，而仅模拟验证为34%。
AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection
Learning robust dexterous grasping requires real-world data that records the physical outcomes of grasp attempts. Such data is hard to obtain at scale: teleoperation yields valid physical outcomes but is slow and operator-biased, while simulation-based generation is cheap and scalable but cannot certify contact validity. A natural solution is to generate candidate grasps and verify them on real hardware, but this scales only if the entire collection loop (perception, execution, labeling, and reset) runs without human intervention. We present AutoDex, an automated real-world data-collection system that closes this loop: for each candidate from a replaceable generator, it localizes the object under severe hand-object occlusion with dense 20-camera perception, executes collision-monitored robot motions, labels lift-and-hold success or failure, and actively resets the object between trials to expose additional candidates across stable poses. The result is a reusable database of physically labeled grasp trials that downstream systems can query by retrieval and feasibility filtering. Using AutoDex, we collect 3,593 grasp trials across Allegro and Inspire hands on 100 diverse objects, with synchronized multi-view observations and robot-state logs. For a matched 500-trajectory collection, AutoDex requires 10.3 h versus 49.4 h for teleoperation, yielding a 4.8x throughput improvement, and grasps retrieved from the AutoDex-validated database succeed 76% versus 34% for simulation-only validation. Code and data will be publicly released.