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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.
We exploit the truck geometry to relate the board tilt angle $\gamma$ to the rotation of the truck axes. Due to the mechanical structure of the trucks, tilting the board by $\gamma$ induces a coordinated rotation of the truck axes, resulting in a kinematic coupling described by:
(a) We first analyze and model the humanoid–skateboard system, deriving a physics-inspired lean-to-steer coupling mechanism.
Due to the distinct contact dynamics and control objectives across skateboarding phases, we adopt a phase-wise learning strategy.
(b) The learning-based whole-body control framework integrates an AMP-based pushing style for active forward propulsion, a steering strategy guided by physics-aware tilt references, and a trajectory-guided transition mechanism to enable stable switching between pushing and steering.
The results demonstrate that HUSKY enables robust humanoid skateboarding with stable, accurate, smooth motions and continuous, reliable phase transitions.
Omitting the equality constraint prevents board tilting from inducing truck steering, leaving the skateboard able only to glide straight forward with negligible turning capability.
Without tilt guidance, the achievable heading range is narrow. In contrast, incorporating tilt guidance produces smooth turning trajectories and enables the humanoid to reach a substantially wider range of headings with higher precision.
In both compared settings, episode length increases rapidly in early training. However, the steering contact reward remains low, indicating persistent errors in foot–board contact patterns.
In contrast,HUSKY discovers correct contact patterns by mid-training, successfully learns foot-mounting transitions, and achieves higher rewards. This demonstrates that trajectory-guided transitions are essential for enabling phase switching and avoiding local optima.
The robot maintains smooth, coordinated whole-body motions with seamless transitions between pushing and steering. The trajectories display consistent foot placement and gradual body pose adjustments, reflecting strong temporal coherence and physical plausibility enabled by our trajectory-guided transitions.
The transition begins with the foot pushing against the ground to generate propulsion, followed by lifting and placing the foot onto the skateboard. Once on the board, the humanoid performs in-place adjustments, rotating the body to align the torso perpendicular to the skateboard deck, thereby facilitating stable steering.
Parameters from a compliant board fail on a stiffer one: simulation exploits board tilt during stepping for mounting, but the real stiff board’s negligible deformation breaks this assumption.
Conversely, applying the stiff-board parameters to the compliant board causes excessive leaning and loss of stability during steering, since the policy is not trained for such compliant dynamics.
