ArXiv

AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection

2026-06-22 · 17:59 UTC ·Mingi Choi, Gunhee Kim, Jisoo Kim... ·1 min read
Authors
Mingi Choi, Gunhee Kim, Jisoo Kim...
Categories
cs.RO, cs.LG
arXiv
https://arxiv.org/abs/2606.23689v1
PDF
https://arxiv.org/pdf/2606.23689v1

Brief

AutoDex is an automated real-world data-collection system for dexterous grasping that generates candidate grasps, localizes heavily occluded objects with a 20-camera rig, executes collision-monitored motions, labels lift-and-hold outcomes, and actively resets objects. Using this loop the authors gathered 3,593 trials on 100 objects (Allegro and Inspire hands), achieving a 4.8× speedup over teleoperation and much higher real-world success (76% vs 34%). Summary based on the paper abstract; full text was not available here.

Why it matters

AutoDex automates the full closed-loop real-world dexterous-grasp data collection pipeline using dense 20-camera perception, collision-monitored robot execution, lift-and-hold success labeling, and active object resets to expose additional stable poses.

Key details

  • The authors collected 3,593 grasp trials across Allegro and Inspire hands on 100 diverse objects, with synchronized multi-view observations and robot-state logs; code and data will be publicly released.
  • For a matched 500-trajectory collection AutoDex required 10.3 h versus 49.4 h for teleoperation (4.8× throughput); grasps retrieved from the AutoDex-validated database succeeded 76% versus 34% for simulation-only validation.
Source evidence

Abstract

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.

Comment: 16 pages, 9 figures. Includes supplementary material