A VLA that Learns from Experience

Introduces Recap, a novel reinforcement learning method that enhances vision-language-action (VLA) models by incorporating expert corrections and autonomous experience alongside initial demonstrations. Recap trains an advantage-conditioned policy by learning a value function to assess task progress and guide learning. This method is applied to the π0.6 VLA model, resulting in π*0.6, which demonstrates more than double the throughput and reduced failure rates on complex robotic tasks, achieving practical real-world performance.