単眼ランウェイ動画からヒューマノイドの表現豊かな歩容を学習しロボットファッションショーへ
Learning Expressive Humanoid Locomotion from Monocular Runway Videos for Robot Fashion Shows
単眼のランウェイ動画から人間のモデル歩きを抽出し、ヒューマノイドロボットで再現する歩容ポリシーを学習・実機展開するフレームワークを提案した。
詳しい要約
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著者: Kyrylo Kolesnichenko, Irvin Steve Cardenas, Jong-Hoon Kim
分類: cs.RO
原文アブストラクト
Runway walking requires coordinated control of posture, stride, foot placement, and whole-body motion to effectively present clothing and convey a distinctive style. However, humanoid robots used in fashion shows typically rely on locomotion policies optimized primarily for stability and walking speed, limiting their ability to reproduce expressive, human-like runway motions. In this work, we present an end-to-end framework that transforms monocular runway videos into deployable humanoid locomotion policies through motion recovery, robot retargeting, motion correction, policy training, simulation-based evaluation, and physical deployment. We evaluate the proposed framework on the Booster K1 humanoid robot using runway-style catwalk motions. The learned policy completed every physical trial without falling, while reproducing the characteristic narrow foot placement and coordinated movement of the legs, torso, and arms. The results demonstrate that our proposed training framework enables the Booster K1 to perform stable and expressive catwalk motions, highlighting its potential for humanoid robotic applications in fashion shows and other performance-oriented scenarios.