Pre-training Visual Dexterity in Simulation
Large-scale pre-training has made robot policy fine-tuning increasingly data-efficient, but this progress has largely been driven by datasets and embodiments built around simple parallel-jaw grippers. Dexterous, multi-fingered hands remain comparatively data-starved because real teleoperation is costly to scale, while human hand video is off-embodiment and requires lossy pose estimation and retargeting. We introduce Simulation Pre-training for Dexterity (SPD), a pre-training framework for dexterous manipulation that uses data entirely collected in simulation. In SPD, humans manipulate virtual objects inside a VR headset, enabling on-embodiment trajectories and robot-free collection. With ...
| Authors | Sarthak Kamat, Adam Rashid, Satvik Sharma et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 August 2026 |
| Last updated at source | 27 August 2026 |
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