Task-Agnostic Pretraining Framework Reduces Expert Data Requirements for Robot Vision-Language Models任务无关预训练框架发布，机器人学习专家数据需求大幅降低
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Researchers propose Task-Agnostic Pretraining (TAP), a two-stage framework for Vision-Language-Action models, submitted to arXiv on July 2, 2026, addressing the bottleneck of scarce and costly expert demonstrations in robot training.
TAP learns transferable motor skills from cheap, unlabeled interaction data (including off-task trajectories and autonomous robot play) via self-supervised Inverse Dynamics, then grounds these priors in language using minimal expert labels, matching performance of models trained on 1M+ trajectories.
On SIMPLER benchmark, TAP achieves 10% absolute improvement over standard behavior cloning; on real-world WidowX robots, it retains 25% success rate under camera perturbations.