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Shanghai Jiao Tong University
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OnEvoMemory allows robots to evolve their memory in real-time, leading to improved task performance and reduced redundancy in long-horizon manipulation tasks.
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.
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.
