pith:6Z5RRGFM
GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data
A grasping model pretrained entirely on a billion-frame synthetic dataset achieves open-vocabulary generalization to real robots by unifying perception and action in one chain-of-thought sequence.
arxiv:2505.03233 v3 · 2025-05-06 · cs.RO
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GraspVLA integrates autoregressive perception tasks and flow-matching-based action generation into a unified Chain-of-Thought process, enabling joint training on synthetic action data and Internet semantics data to achieve open-vocabulary generalization in grasping.
That photorealistic rendering plus extensive domain randomization in simulation, combined with the CoT architecture, is sufficient to close the sim-to-real gap so that actions learned on synthetic data transfer effectively to physical robots on unseen objects.
GraspVLA shows that pretraining a grasping model on a billion synthetic action frames enables zero-shot open-vocabulary performance and sim-to-real transfer.
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| First computed | 2026-05-17T23:38:13.102573Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/6Z5RRGFM332ONHQZVDEEGS642O \
| jq -c '.canonical_record' \
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Canonical record JSON
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