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CapPro: Adapting VLA models to new manipulation tasks with a single human video

Researchers propose the CapPro framework, which lets a vision-language-action (VLA) policy adapt to previously unseen manipulation tasks using only a single human demonstration video, without robot demonstrations or fine-tuning for the target task. The framework has two components: a phase-aware human video representation module, which learns task-relevant query representations through visual token selection and phase-caption supervision, and a video-conditioned adaptation module, which injects the compact video and query representations into the action-conditioning context of a pretrained VLA policy.

Part of the event: CapPro: Adapting VLA to New Tasks from a Single Human Video

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