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Task-Agnostic Pretraining enables VLA models to achieve expert-level performance with orders of magnitude less labeled data, revolutionizing the scalability of embodied AI.
Robots can now autonomously adapt to new environments without retraining, using just a history of their own interactions.
Existing robotic methods falter in tackling fundamental physical reasoning challenges, as evidenced by KinDER's rigorous benchmark evaluation.