Robotics: Science and Systems XXII

HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control

Jinrui Han, Dewei Wang, Chenyun Zhang, Xinzhe Liu, Ping Luo, Chenjia Bai, Xuelong Li

Abstract:

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 a skateboard in real-world scenarios.

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Bibtex:

  
@INPROCEEDINGS{HanJ-RSS-26, 
    AUTHOR    = {Jinrui Han AND Dewei Wang AND Chenyun Zhang AND Xinzhe Liu AND Ping Luo AND Chenjia Bai AND Xuelong Li}, 
    TITLE     = {{HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.019} 
}