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Skild AI Introduces S1 Robotics Foundation Model with In-Context Video Prompting

Robotics foundation model startup Skild AI has unveiled S1, a foundation model capable of learning physical manipulation tasks unseen during pretraining directly from a single video demonstration prompt without fine-tuning. Traditional robotic adaptation typically requires extensive task-specific teleoperation data, domain randomization, and model fine-tuning before a system can reliably execute novel actions. S1 employs in-context prompting to translate visual demonstrations directly into real

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axc
1 min
Skild AI Introduces S1 Robotics Foundation Model with In-Context Video Prompting

Robotics foundation model startup Skild AI has unveiled S1, a foundation model capable of learning physical manipulation tasks unseen during pretraining directly from a single video demonstration prompt without fine-tuning.

Traditional robotic adaptation typically requires extensive task-specific teleoperation data, domain randomization, and model fine-tuning before a system can reliably execute novel actions. S1 employs in-context prompting to translate visual demonstrations directly into real-time control policies, enabling robots to follow multi-step physical instructions up to ten minutes in duration.

Skild AI S1 Hierarchical Architecture

Benchmark Performance and Sample Efficiency

In evaluations measuring zero-shot execution on novel manipulation sequences:

  • Step-success rate: S1 recorded a 66% step-success rate on unseen manipulation tasks when prompted with a video demonstration, compared to 9% achieved by a language-prompted baseline model.
  • Demonstration efficiency: Skild AI reported that a single video demonstration delivered behavioral guidance roughly equivalent to 380 post-training physical robot examples.
  • In-context execution: The model processes video inputs as contextual prompts during inference, bypassing the need to update underlying neural network weights.

Hierarchical Architecture and Multi-Embodiment Training

S1 builds on Skild AI's omni-bodied foundation architecture, designed to decouple general task planning from embodiment-specific motor execution:

  • Two-tier policy structure: The architecture splits control into a low-frequency, high-level manipulation and navigation policy that outputs strategic objectives, and a high-frequency, low-level action policy that converts those objectives into joint angles and motor torques.
  • Cross-embodiment generalization: The system is pre-trained across diverse robot morphologies, including quadrupeds, humanoid systems, and tabletop manipulators, leveraging simulation data alongside internet video.
  • Scalable physical AI: By separating physical actuation from visual task representation, the system aims to deploy on cost-effective commercial hardware without requiring custom model architectures for each physical configuration.

Sources

RoboticsFoundation ModelsEmbodied AI
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