用参数化方法生成自然人形行走轨迹，支持实时控制与仿真到现实的迁移。
PMG: Parameterized Motion Generator for Human-like Locomotion Control
- 基于人体运动结构分析，仅用少量参数化数据生成参考轨迹。
- 在ZERITH Z1机器人上实现自然行走，对高维控制输入响应精准。
- 支持VR遥操作，具备可验证的仿真到现实迁移能力，适合部署应用。
近年来，数据驱动的强化学习和运动追踪技术显著提升了类人机器人行走能力，但实际应用仍面临挑战：尽管底层运动追踪和轨迹跟随控制器已成熟，全身参考引导方法难以适配高层命令接口和多样任务场景，需大量高质量数据，对速度和姿态变化敏感，且依赖机器人特异性校准。为此，我们提出参数化运动生成器（PMG），一种基于人体运动结构分析的实时运动生成方法，仅需少量参数化运动数据和高维控制指令即可合成参考轨迹。结合模仿学习流程与基于优化的仿真到现实电机参数识别模块，在类人机器人原型ZERITH Z1上验证了完整方案。结果表明，单一集成系统下，PMG可生成自然、类人行走动作，精确响应高维控制输入（包括基于VR的遥操作），并实现高效、可验证的仿真到现实迁移。这些成果为自然、可部署的人形机器人控制提供了实验验证的实用路径。
原文摘要 · Abstract (English)
Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain. In particular, while low-level motion tracking and trajectory-following controllers are mature, whole-body reference-guided methods are difficult to adapt to higher-level command interfaces and diverse task contexts: they require large, high-quality datasets, are brittle across speed and pose regimes, and are sensitive to robot-specific calibration. To address these limitations, we propose the Parameterized Motion Generator (PMG), a real-time motion generator grounded in an analysis of human motion structure that synthesizes reference trajectories using only a compact set of parameterized motion data together with high-dimensional control commands. Combined with an imitation-learning pipeline and an optimization-based sim-to-real motor parameter identification module, we validate the complete approach on our humanoid prototype ZERITH Z1 and show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs-including VR-based teleoperation-and enables efficient, verifiable sim-to-real transfer. Together, these results establish a practical, experimentally validated pathway toward natural and deployable humanoid control. Website: https://pmg-icra26.github.io/
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