task-agnostic-pretraining

Task-Agnostic Pretraining (TAP) decomposes VLA training into self-supervised motor skill learning from unlabeled interaction data, then lightweight language grounding, achieving strong performance with minimal expert demonstrations. It matches or outperforms models trained on millions of expert trajectories while being robust to real-world perturbations.