Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video

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 video as input to understand and execute the task without updating its weights or undergoing task-specific post-training — a technique called in-context learning.  

Skild built S1 and conducted the research on NVIDIA AI infrastructure, part of a broader collaboration spanning synthetic data generation, model training, simulation and real-world physical AI deployment. The companies are working together to move adaptable robot intelligence from the lab into factories and other dynamic operating environments.

“Learning by experience, and not preprogramming, is the step change that has happened in robotics,” said Deepak Pathak, cofounder and CEO of Skild AI. “NVIDIA Isaac Lab and NVIDIA Cosmos technologies help Skild create the scalable, diverse experience its robots need to learn across many scenarios and embodiments.”

The launch comes as the company reached a $100 million annual revenue run rate 10 months after its first commercial deployment. In that time, Skild has built more than 60 deployment partnerships with work spanning manufacturing, logistics, inspection, security, food preparation and other applications. 

Learning New Work From One Video

Most industrial robots are built for fixed jobs, so each new product, process or layout requires more data, retraining and validation.

S1 takes a different approach: An operator records a video of the desired task and provides it to the model as a prompt. It interprets the demonstrated intent, objects and sequence, then maps them into actions for the robot in front of it — with no retraining — and often for a task not covered by its pretraining dataset.