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MANUS™
@ManusMeta
Building scalable solutions for human interaction data. Join our team:
加入 June 2014
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New study from @Stanford , @MIT , and @scale_AI , "Pre-training Visual Dexterity in Simulation," pre-trained a bimanual robot policy on 75 hours of simulated demonstrations, then fine-tuned it with just one to two hours of real data per task. For the fine-tuning stage, operators wore MANUS gloves for finger tracking paired with a Quest controller for wrist tracking, producing the smoother, more precise control needed to guide the robot through occlusions and at a distance. Read more:
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