Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video
Skild AI launched its S1 robot foundation model, built on NVIDIA infrastructure, that learns long-horizon industrial tasks from a single video.
Skild AI's S1 model uses in-context learning from one video demonstration to execute unfamiliar multistep tasks lasting up to 10 minutes without weight updates or task-specific post-training. In tests on new tasks it achieved about 66% per-step success versus 9% for a comparable AI system, and one video demonstration was estimated to match roughly 380 hands-on training examples. The company reached a $100 million annual revenue run rate with more than 60 deployment partnerships, and with NVIDIA and Foxconn deploys the Skild Brain on dual-arm manipulators assembling NVIDIA Blackwell systems. Training and validation rely on NVIDIA Isaac Lab, Isaac Sim, Omniverse, Cosmos and the Newton physics engine.
- S1 performs tasks up to 10 minutes from one video demo without retraining
- 66% per-step success on new tasks versus 9% for a comparable AI system
- Skild hit a $100M annual revenue run rate 10 months after first deployment
- Skild Brain deployed with Foxconn for dual-arm assembly of NVIDIA Blackwell systems
- AI models
- S1
From the original article
4 min read at blogs.nvidia.com
Manufacturing floors, warehouses and production lines rarely stay fixed — tasks change, layouts shift and new products arrive, and most robots can’t keep up without significant reprogramming. Skild AI’s new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses […]
By Sasa Docca · blogs.nvidia.com
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