pith:UCFL54T5
Vidar: Embodied Video Diffusion Model for Generalist Manipulation
A video diffusion model pre-trained on internet-scale data and 750K robot trajectories adapts to new robot embodiments with only 20 minutes of demonstrations.
arxiv:2507.12898 v4 · 2025-07-17 · cs.LG · cs.AI · cs.CV · cs.RO
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Claims
With only 20 minutes of human demonstrations on an unseen robot (1% of typical data), Vidar outperforms state-of-the-art baselines and generalizes to unseen tasks, backgrounds, and camera layouts.
That continuous pre-training of an internet-scale video diffusion model on 750K trajectories from only three robot platforms produces a sufficiently general visual-dynamics prior that can be grounded to arbitrary new embodiments via a lightweight masked inverse dynamics adapter.
Vidar shows that a video diffusion prior continuously pre-trained on 750K multi-view robot trajectories plus a label-free masked inverse dynamics adapter can generalize manipulation to new robot embodiments with 1% of typical demonstration data.
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| First computed | 2026-05-17T23:38:48.275613Z |
|---|---|
| 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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Canonical record JSON
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