We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a multi-contact, whole-body trajectory optimization problem. Conventional kinematics-based retargeting methods rely solely on spatial motion capture (MoCap) data, inevitably introducing physically inconsistent artifacts, such as foot sliding and ground penetration, that severely degrade the performance of downstream imitation learning policies. To bridge this gap, KDMR extends beyond pure kinematics by explicitly enforcing rigid-body dynamics and contact complementarity constraints. Further, by integrating ground reaction force (GRF) measurements alongside MoCap data, our method automatically detects heel-toe contact events to accurately replicate complex human-like contact patterns. We evaluate KDMR against the state-of-the-art baseline, GMR, across three key dimensions: 1) the dynamic feasibility and smoothness of the retargeted motions, 2) the accuracy of GRF tracking compared to raw source data, and 3) the training efficiency and final performance of downstream control policies trained via the BeyondMimic framework. Experimental results demonstrate that KDMR significantly outperforms purely kinematic methods, yielding dynamically viable reference trajectories that accelerate policy convergence and enhance overall locomotion stability. Our end-to-end pipeline will be open-sourced upon publication.

翻译：本文提出KinoDynamic Motion Retargeting（KDMR）框架，这是一种用于类人机器人运动的新型方法，将重定向过程建模为多接触全身轨迹优化问题。传统基于运动学的重定向方法仅依赖空间运动捕捉数据，不可避免地引入物理不一致的伪影（如足部滑动和地面穿透），严重降低下游模仿学习策略的性能。为弥补这一缺陷，KDMR通过显式强化刚体动力学和接触互补约束，超越了纯运动学范畴。进一步地，通过整合地面反作用力测量数据与运动捕捉数据，本方法能自动检测足跟-足尖接触事件，精确复现复杂类人接触模式。我们在三个关键维度上将KDMR与前沿基线方法GMR进行对比评估：1）重定向运动的动态可行性与平滑度；2）与原始源数据相比的地面反作用力跟踪精度；3）通过BeyondMimic框架训练的下游控制策略的训练效率与最终性能。实验结果表明，KDMR显著优于纯运动学方法，能生成动态可行的参考轨迹，加速策略收敛并提升整体运动稳定性。我们的端到端流程将在发表后开源。