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State-aware tokenization can double the success rate in robotic manipulation tasks by adapting actions to the robot's current state.
Human-in-the-loop chunk-wise residual adaptation closes the reality gap for dexterous robot manipulation, boosting success rates by up to 43% compared to offline imitation learning.
Robot control systems are shockingly vulnerable: JailWAM achieves an 84.2% success rate in jailbreaking state-of-the-art World Action Models to perform unsafe physical actions.
Human-in-the-loop learning can now boost dexterous manipulation VLA models by 25%, thanks to a new framework that smartly samples corrective actions and enables real-time intervention.
