外观
论文arXiv 2610.0838122 天前
从腿到轮：面向移动底座人形机器人的具身感知人体运动重定向
原文链接：From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids（arXiv 2610.08381 · 发布日期：2026-10-06）
From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids
针对轮式移动底座人形机器人与人体运动不匹配，提出任务条件重定向框架，并在任务空间比较、仿真回放和真机定性执行中评估。
核心要点
- 问题在于现有人体到人形机器人重定向多假设腿式、类人运动学，不适用于轮式底座人形机器人。
- 方法先将重建人体运动分配给底座、升降和手臂职责，再进行面向具体机器人的实现。
- 分配器保留人体路径、稳定朝向、必要时分离转向和平移，并修复升降与手臂轨迹。
- 部署适配器将参考转换为50 Hz命令，并采用静止底座检测、滤波、播放缩放和分离增益。
验证范围与局限：验证包括人体派生任务空间比较、无策略仿真回放，以及一台物理机器人的定性执行。
英文摘要 (Abstract)
Human video offers a scalable source of robot demonstrations, yet most human-to-humanoid retargeting methods assume a legged robot with human-like kinematics. This assumption does not hold for mobile-base humanoids equipped with a wheeled base, vertical lift, and two arms. Human walking must be expressed through base motion, while torso bending may require coordinated lift and arm motion. We address this mismatch with a task-conditioned framework that assigns reconstructed human motion to base, lift, and arm responsibilities before robot-specific realization. The allocator preserves the human-derived path, stabilizes heading, separates turn and translation when needed, retimes commands to satisfy base limits, and repairs lift and arm trajectories. A deployment adapter then converts the reference to 50 Hz commands using stationary-base detection, deadband and slew-rate filtering, time-consistent playback scaling, and separate linear and angular gains. We evaluate the resulting references with human-derived task-space comparisons, policy-free simulation replay, and a qualitative execution on a physical robot.
作者：Jiyeon Koo, Eunseom Pyo, Jeonghee Seo, Kim, Taehwa, Andrew Jaeyong Choi
来源
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