Skild S1 (Skild Brain)
Announced on X 2026-08-25 (https://x.com/SkildAI/status/2092300842900865389); press 2026-08-31; NVIDIA blog 2026-09-10 (https://blogs.nvidia.com/blog/skild-ai-s1-physical-ai/) cites a $100M revenue run rate 10 months after first commercial deployment, 60+ deployment partnerships and Blackwell assembly work with Foxconn. Skild raised a $1.4B Series C at >$14B (2026-01-14, led by SoftBank). No public API, pricing or weights; company says S1 is "already at work with our commercial partners" and plans wider real-world rollout by 2027. Results are company-reported.
- Input
- video, image, text
- Output
- action
- License
- proprietary
How to call it
| Provider | Model id | Endpoint / URL | Docs |
|---|---|---|---|
| Skild AI (commercial partners; early-access sign-up) | — | www.skild.ai/blogs/s1 | — |
Notable capabilities (3)
- FIRST In-context learning from one video, long-horizon: Learns tasks never seen in pretraining (potting a plant, cooking pancakes, pour-over coffee, kit assembly) from a single video prompt with no fine-tuning, for tasks up to ~10 minutes long; Skild calls this the first robotics foundation model to show in-context learning on such long unseen tasks. source
- Video prompting beats language prompting: 66% success on unseen tasks vs 9% for an equivalently trained language-prompted policy (~7x); 96% on seen tasks; one demo video worth ~380 post-training episodes; 11 minutes from demonstration to autonomous execution in the plant-potting example. source
- Omni-bodied brain: Skild Brain is pitched as one model controlling quadrupeds, humanoids, arms and mobile manipulators without prior knowledge of the body; S1 trains on teleop, human video, simulation and data-capture gloves. source
Timeline entry
- Skild AI's S1 learns 10-minute robot tasks from a single video prompt ★★★★
Skild AI unveiled S1 on 2026-08-25, a robot foundation model that performs unseen long-horizon tasks (up to ~10 minutes, e.g. pancakes, pour-over coffee, potting a plant) from one video demonstration with no fine-tuning, reaching 66% success on unseen tasks vs 9% for a language-prompted policy.