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Skild AI's S1 foundation model demonstrates a significant shift in robotics by leveraging in-context learning from single video demonstrations to execute long-horizon tasks without retraining weights. This approach, powered by NVIDIA Cosmos technologies and Isaac Sim for virtual validation, effectively bypasses the traditional bottleneck of extensive manual data collection while enabling rapid adaptation to dynamic manufacturing environments.
Skild AI robot learns new factory tasks from a single video
Skild AI’s S1 robot foundation model learns previously unseen, long-horizon tasks from a single video demonstration, understanding and executing the task without updating its weights or undergoing task-specific post-training. The model uses video as input, a technique called in-context learning, to map demonstrated intent into actions for the robot.
Just ten months after its first commercial deployment, Skild AI has reached a $100 million annual revenue run rate while establishing over 60 partnerships across manufacturing, logistics, and even food preparation. The company’s new S1 robot foundation model achieves adaptability by learning previously unseen tasks from a single video demonstration, eliminating the need for reprogramming, Skild AI says. “Learning by experience, and not preprogramming, is the step change that has happened in robotics,” says Deepak Pathak, cofounder and CEO of Skild AI, as the company collaborates with NVIDIA to deploy…
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