Teaching robot hands to learn from mistakes in the real world

VLA models can see and describe objects, but controlling a robot hand precisely remains hard. BORA first trains a critic offline using visual tokens and action sequences, then lets humans correct the robot's movements in real deployments—freezing the base model while only adapting lightweight residual actions. Five complex tasks show 33% absolute success-rate gains, with 43% better performance on unseen objects.