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This work studies open-vocabulary task-oriented dexterous grasp generation, where a robot must infer functional intent from free-form language, ground it in multi-view visual observations and object geometry, and generate an executable high-degree-of-freedom grasp.
An on-policy expert-correction pipeline is developed, automated by a meta-level MLE agent, that localizes the failing turn in the weaker model's own rollout and asks the expert to rewrite only that turn, which preserves the model's planning style and combines the gains of harness evolution and model adaptation.
VoRTeC slashes bit consumption by 58% while boosting decoding speeds up to 197 times, revolutionizing real-time video compression.
TeleDexter achieves a remarkable 75% success rate in dexterous teleoperation tasks, where existing systems fail, showcasing a leap towards human-level control in robotic manipulation.
