SPARK: ヒューマノイドロボットにおける骨格パラメータ整合リターゲティングと運動力学的軌道最適化
SPARK: Skeleton-Parameter Aligned Retargeting on Humanoid Robots with Kinodynamic Trajectory Optimization
人間の動作データをヒューマノイドロボット用に変換する2段階パイプラインを提案。骨格構造をロボットに合わせて較正し、運動力学的軌道最適化で動的に実現可能な自然な動作参照を生成する。
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著者: Hanwen Wang, Qiayuan Liao, Bike Zhang, Kunzhao Ren, Koushil Sreenath, Xiaobin Xiong
分類: cs.RO
原文アブストラクト
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.
