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Seoul National University
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AutoDex accelerates dexterous grasping data collection by 4.8 times while significantly improving grasp success rates from simulation-only validation.
Achieving reliable long-horizon manipulation without prior training on specific tasks, this framework enables robots to adaptively execute complex actions in new environments based solely on language instructions.
