Phantom: Training Robots Without Robots Using Only Human Videos

Understand

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.