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REACT: VLM-Guided Contact Retargeting and Generative Partner Modeling for Humanoid-Human Interaction

REACT is a framework for learning deployable humanoid-human interaction policies from human-human demonstrations, using semantic contact structure as the supervisory signal that links demonstration processing, interaction learning, and onboard control. It uses a VLM-guided contact modeling and retargeting pipeline to identify semantic contacts and transfer them onto humanoid-human geometry, then distills a privileged interaction policy into an egocentric depth policy that relies entirely on onboard sensing, and introduces a generative partner state modeling objective to predict partner motion from occluded viewpoints.

Part of the event: REACT framework enables humanoid robots to learn interaction policies from human-human demonstrations

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