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

2026-02-03

AI Summary

This work addresses the challenge of achieving stable and agile control of humanoid robots on dynamically unstable, underactuated skateboard platforms. Moving beyond conventional whole-body control methods that rely on static-environment assumptions, the authors formulate a nonholonomic-constrained dynamics model of the coupled humanoid-skateboard system. They propose a learning strategy that integrates physical constraints with adversarial motion priors (AMP) to generate human-like pushing motions. Furthermore, a heading-guided leaning steering mechanism and a trajectory-guided action transition method are designed to enable smooth maneuvering. The approach is validated on the Unitree G1 platform, where it demonstrates, for the first time, real-time, stable, and agile skateboard control in the real world, confirming its effectiveness and robustness in dynamic environments.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 skateboards in real-world scenarios. The project page is available on https://husky-humanoid.github.io/.

Problem

• humanoid skateboarding
• whole-body control
• non-holonomic constraints
• dynamic balance
• human-object interaction

Innovation

• whole-body control
• humanoid skateboarding
• physics-aware learning
• non-holonomic constraints
• Adversarial Motion Priors