SPARK：基于骨架-参数对齐的人形机器人运动重定向与运动学轨迹优化
SPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization
AI总结：
SPARK通过骨架对齐和运动学轨迹优化，从人类任务空间数据生成自然且动态可行的运动参考，适用于不同人形机器人平台。
摘要：
人类运动为训练通用人形机器人控制策略提供了丰富的先验信息，但原始示范通常与机器人的运动学和动力学不兼容，限制了其直接使用。我们提出了一种两阶段流程，从任务空间的人类数据生成自然且动态可行的运动参考。首先，我们将人类运动转换为基于URDF的统一机器人描述格式的骨架表示，并校准其到目标人形机器人的尺寸。通过对齐底层骨架结构而非启发式修改任务空间目标，这一步显著减少了逆运动学误差和调参努力。第二，我们通过逐步的运动学轨迹优化（TO）来优化重定向轨迹，该优化分为三个阶段：运动学TO、逆动力学和完整的运动学TO，每个阶段都从前一阶段的解进行预热。最终结果产生动态一致的状态轨迹和关节扭矩剖面，为基于学习的控制器提供高质量的参考。共同，骨架校准和运动学TO能够在不同的人形机器人平台上生成自然且物理一致的运动参考。
英文摘要：
Human motion provides rich priors for training general-purpose humanoid control policies, but raw demonstrations are often incompatible with a robot's kinematics and dynamics, limiting their direct use. We present a two-stage pipeline for generating natural and dynamically feasible motion references from task-space human data. First, we convert human motion into a unified robot description format (URDF)-based skeleton representation and calibrate it to the target humanoid's dimensions. By aligning the underlying skeleton structure rather than heuristically modifying task-space targets, this step significantly reduces inverse kinematics error and tuning effort. Second, we refine the retargeted trajectories through progressive kinodynamic trajectory optimization (TO), solved in three stages: kinematic TO, inverse dynamics, and full kinodynamic TO, each warm-started from the previous solution. The final result yields dynamically consistent state trajectories and joint torque profiles, providing high-quality references for learning-based controllers. Together, skeleton calibration and kinodynamic TO enable the generation of natural, physically consistent motion references across diverse humanoid platforms.
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