Learning Expressive Humanoid Locomotion from Monocular Runway Videos for Robot Fashion Shows

2026-09-22

AI Summary

研究通过单目跑道视频学习人类行走方式，转化为机器人可用的行走策略，使Booster K1机器人能够稳定且有表现力地走秀。

Abstract

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.

Problem

• humanoid robots
• fashion shows
• expressive locomotion
• runway walking

Innovation

• end-to-end framework
• monocular runway videos
• humanoid locomotion policies
• motion recovery
• robot retargeting