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Language models can be trained to proactively avoid costly errors by learning to "fail gracefully" directly from outcome-level rewards, without needing explicit safety annotations or verifiers.
Humanoid locomotion learned from scratch and transferred to hardware in 10x less time, thanks to a novel off-policy flow RL approach.
The ABC framework empowers researchers with the largest open-source teleoperation dataset and a complete toolkit to accelerate advancements in behavior cloning for robotic manipulation.