Skild AI's S1 learns robot tasks from one video, hits $100M revenue run rate
NVIDIA Blog · rss · 2026-09-11
Skild AI launched its S1 robot foundation model in collaboration with NVIDIA, designed to let robots adapt to changing factory work without reprogramming.
Core capability:
- Single-video teaching: an operator records a task; S1 uses in-context learning to interpret intent, objects, and step sequences — no weight updates or task-specific post-training.
- Handles unfamiliar long-horizon tasks up to 10 minutes (plant potting, pancakes, pour-over coffee, kit assembly) spanning dozens of manipulation steps.
- Demo-to-autonomous-execution in 11 minutes; adapts to moved objects, recovers from errors, composes unprogrammed skill sequences.
Numbers: 66% per-step success on new multistep tasks vs. 9% for a comparable system (7x+); one short video ≈ 380 hands-on training examples, which would take 50–100 hours to collect manually.
Commercial traction: $100M annual revenue run rate 10 months after first deployment, 60+ deployment partnerships across manufacturing, logistics, inspection, security, and food prep. Skild, NVIDIA, and Foxconn are deploying Skild Brain on dual-arm manipulators for high-precision Blackwell system assembly.
Stack: NVIDIA Cosmos for synthetic data and video structuring, Isaac Sim/Omniverse for simulation and edge-case testing, Isaac Lab (Newton physics engine) for RL to close the sim2real gap, TensorRT for inference latency. Joint GPU-accelerated contact/grasp solvers will ship in Newton for all developers.
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