While current humanoid whole-body control frameworks predominantly rely on the static environment assumptions, addressing tasks characterized by high dynamism and complex interactions presents a formidable challenge. In this paper, we address humanoid skateboarding, a highly challenging task requiring stable dynamic maneuvering on an underactuated wheeled platform. This integrated system is governed by non-holonomic constraints and tightly coupled human-object interactions. Successfully executing this task requires simultaneous mastery of hybrid contact dynamics and robust balance control on a mechanically coupled, dynamically unstable skateboard. To overcome the aforementioned challenges, we propose HUSKY, a learning-based framework that integrates humanoid-skateboard system modeling and physics-aware whole-body control. We first model the coupling relationship between board tilt and truck steering angles, enabling a principled analysis of system dynamics. Building upon this, HUSKY leverages Adversarial Motion Priors (AMP) to learn human-like pushing motions and employs a physics-guided, heading-oriented strategy for lean-to-steer behaviors. Moreover, a trajectory-guided mechanism ensures smooth and stable transitions between pushing and steering. Experimental results on the Unitree G1 humanoid platform demonstrate that our framework enables stable and agile maneuvering on skateboards in real-world scenarios. The project page is available on https://husky-humanoid.github.io/.

翻译：尽管当前的人形机器人全身控制框架主要依赖于静态环境假设，但处理具有高度动态性和复杂交互特征的任务仍是一项艰巨挑战。本文针对人形滑板这一极具挑战性的任务展开研究，该任务要求在不完全驱动的轮式平台上实现稳定的动态操控。该集成系统受非完整约束支配，并涉及紧密耦合的人-物交互。成功执行此任务需要同时掌握混合接触动力学以及在机械耦合、动态不稳定的滑板上实现鲁棒的平衡控制。为克服上述挑战，我们提出HUSKY——一个融合人形-滑板系统建模与物理感知全身控制的学习框架。我们首先建立了板面倾斜与转向轴角度之间的耦合关系模型，从而实现对系统动力学的原理性分析。在此基础上，HUSKY利用对抗运动先验（AMP）学习类人推动动作，并采用物理引导的航向导向策略实现倾斜转向行为。此外，轨迹引导机制确保了推动与转向动作间的平滑稳定过渡。在宇树G1人形机器人平台上的实验结果表明，我们的框架能够在真实场景中实现稳定敏捷的滑板操控。项目页面详见 https://husky-humanoid.github.io/。