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arXiv · 2503.00779

Phantom: Training Robots Without Robots Using Only Human Videos

Marion Lepert·Jiaying Fang·Jeannette Bohg
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Abstract

Training general-purpose robots requires learning from large and diverse data sources. Current approaches rely heavily on teleoperated demonstrations which are difficult to scale. We present a scalable framework for training manipulation policies directly from human video demonstrations, requiring no robot data. Our method converts human demonstrations into robot-compatible observation-action pairs using hand pose estimation and visual data editing. We inpaint the human arm and overlay a rendered robot to align the visual domains. This enables zero-shot deployment on real hardware without any fine-tuning. We demonstrate strong success rates-up to 92%-on a range of tasks including deformable object manipulation, multi-object sweeping, and insertion. Our approach generalizes to novel environments and supports closed-loop execution. By demonstrating that effective policies can be trained using only human videos, our method broadens the path to scalable robot learning.

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Marion Lepert, Jiaying Fang, Jeannette Bohg. 2025-03-02. Phantom: Training Robots Without Robots Using Only Human Videos. https://arxiv.org/abs/2503.00779

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