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HKU's EgoWild2Dex trains dual-arm robot on 538.9 hours of "extremely dirty" human data, with under 1 hour of real-robot data

The University of Hong Kong's MMLab team proposes EgoWild2Dex, which directly trains a dual-arm dexterous robot on 538.9 hours of unscripted human first-person manipulation data, using under 1 hour of real-robot data per downstream task; across three long-horizon tasks—unpacking a parcel, fixing ornaments with a hot glue gun, and making a cold drink—the average success rate is 96.7%.

Part of the event: HKU trains dual-arm robot using 538.9 hours of human data

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