AI 中文总结
研究人形通用运动跟踪中重要因素，开发YAHMP框架，比较不同建模和训练因素变体，在测试集评估策略并与基线对比，区分不同选择效果，还将策略零样本部署到真实机器人展示多种能力。
AI 中文摘要
人形通用运动跟踪需要能遵循多样全身参考同时保持平衡的策略。构建此类策略涉及诸多实际设计选择，其各自效果常难评估。我们通过对近期人形运动模仿流程中常见建模和训练因素的实证研究来解决此问题。为使研究可控且可重复，我们开发了YAHMP，一个用于在Unitree G1上训练、评估和部署全身运动跟踪策略的开源模块化框架。在YAHMP内，我们定义了一个标称配置，并比较了在运动命令表示、观察历史、动作表示、驱动配置文件、训练期间手部力随机化以及训练方法等方面不同的变体。我们在一组重定向人类运动的测试集上评估所得策略，并将标称策略与在同一运动集上训练的TWIST2作为外部基线进行比较。结果区分了具有明显跟踪效果的选择与主要改变驱动努力、训练复杂性或物理交互能力的选择。最后，我们将YAHMP策略零样本部署到真实的Unitree G1上，展示了多样的全身运动跟踪、外部扰动下的平衡以及有力的交互。
英文摘要
Humanoid general motion tracking requires policies that can follow diverse whole-body references while maintaining balance. Building such policies involves many practical design choices, and their individual effects are often hard to assess. We address this issue with an empirical study of common modeling and training factors used in recent humanoid motion-imitation pipelines. To make the study controlled and reproducible, we developed YAHMP, an open-source modular framework for training, evaluating, and deploying whole-body motion tracking policies on the Unitree G1. Within YAHMP, we define a nominal configuration and compare variants that differ in motion-command representation, observation history, action representation, actuation profile, hand-force randomization during training, and training approach. We evaluate the resulting policies on a test set of retargeted human motions and compare the nominal policy with TWIST2 as an external baseline trained on the same motion set. The results distinguish choices with clear tracking effects from choices that mainly change actuation effort, training complexity, or physical interaction capability. Finally, we deploy YAHMP policies zero-shot on the real Unitree G1, demonstrating diverse whole-body motion tracking, balance under external perturbations, and forceful interaction.
