发表机构
Vilnius University; Kent State University（维尔纽斯大学; 肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出端到端框架，将单目跑道视频转化为可部署的人形机器人步态策略，使Booster K1在物理试验中稳定执行富有表现力的猫步动作。
AI 中文摘要
跑道行走需要协调控制姿态、步幅、脚步位置和全身运动，以有效展示服装并传达独特风格。然而，用于时装秀的人形机器人通常依赖于主要针对稳定性和行走速度优化的步态策略，限制了其复现富有表现力、类人跑道动作的能力。在本工作中，我们提出了一种端到端框架，通过动作恢复、机器人重定向、动作修正、策略训练、基于仿真的评估和物理部署，将单目跑道视频转化为可部署的人形机器人步态策略。我们在Booster K1人形机器人上使用跑道风格的猫步动作评估了所提出的框架。学习到的策略在每次物理试验中均未摔倒，同时复现了特征性的窄脚步放置以及腿、躯干和手臂的协调运动。结果表明，我们提出的训练框架使Booster K1能够执行稳定且富有表现力的猫步动作，突显了其在时装秀及其他表演导向场景中人形机器人应用的潜力。
英文摘要
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.
CommentsIROS 2026 Poster Paper
