形态感知的轮式人形机器人运动重定向用于移动操作
Morphology-Aware Human Motion Retargeting for Wheeled-Humanoid Loco-Manipulation
- Harbin Institute of Technology（哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种形态感知的流水线，将SMPLX人体运动重定向为轮式人形机器人R1 Pro的可执行移动操作行为，结合逆运动学与强化学习策略，实现从数据到物理跟踪的完整桥梁。
AI中文摘要：
人形到人形机器人的运动重定向研究主要集中在足式平台上，而相对较少的轮式人形机器人系统支持从一般人体运动中进行耦合的移动与操作。基于GMR的可配置通用运动重定向和BeyondMimic的物理仿真R1 Pro学习框架，我们提出了一种可复现的流水线，将多数据集SMPLX运动转换为Galaxea R1 Pro轮式人形机器人的可执行移动操作行为。该机器人具有平面三轮底盘、串联躯干和双臂，但没有腿部关节，因此人体下半身运动必须重新分配到底盘运动和躯干姿态中，同时不牺牲与操作相关的手臂几何结构。我们的流水线结合了规范体型预处理、平面底盘归一化、形态感知的微分逆运动学、肩根层次化手臂重定向，以及用于弯腰和蹲下的连续躯干替代。随后，一个参考扭转驱动的规划层将平面底盘运动解码为连续的三轮转向和滚动命令，并受滞回、运动学连续性、加速度和执行器速率限制的约束。最后，在Isaac Lab中训练了一个21维的BaseDecode策略，采用定向关节极限缩放、重点上半身跟踪和分阶段轮地接触奖励。所得到的系统提供了从人体运动数据到物理可跟踪的轮式人形机器人移动操作的完整桥梁，而不仅仅是可视化重定向器；定量策略比较计划在后续修订中发布。
英文摘要：
Human-to-humanoid retargeting has largely been studied on legged platforms, while comparatively few wheeled-humanoid systems support coupled locomotion and manipulation from general human motion. Building on GMR's configurable general-motion retargeting and BeyondMimic's physically simulated R1 Pro learning framework, we present a reproducible pipeline that converts multi-dataset SMPLX motion into executable loco-manipulation behavior for the Galaxea R1 Pro wheeled humanoid. The robot has a planar three-wheel base, a serial torso, and two arms but no leg joints, so human lower-body motion must be redistributed across base motion and torso posture without sacrificing manipulation-relevant arm geometry. Our pipeline combines canonical body-shape preprocessing, planar-base normalization, morphology-aware differential inverse kinematics, shoulder-rooted hierarchical arm retargeting, and continuous torso substitution for bending and squatting. A reference-twist-driven planning layer then decodes planar base motion into continuous three-wheel steering and rolling commands subject to hysteresis, kinematic continuity, acceleration, and actuator-rate limits. Finally, a 21-dimensional BaseDecode policy is trained in Isaac Lab with directional joint-limit scaling, focused upper-body tracking, and a staged wheel-contact reward. The resulting system provides a complete bridge from human motion data to physically trackable wheeled-humanoid loco-manipulation rather than a visualization-only retargeter; quantitative policy comparisons remain scheduled for a later revision.
