Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
This paper presents a hierarchical control framework using model predictive control (MPC) and reinforcement learning (RL) for active roll control to manage lateral load transfer during autonomous racing of a wheeled quadruped. The framework integrates offline time-optimal raceline generation, an online MPC planner that actively minimizes the lateral Load Transfer Ratio (LTR), and a low-level, whole-body RL policy deployed directly onto the robot's 16 actuators. Physical experiments show that active roll control reduces mean LTR by up to 44%, improves fastest lap time by 8.7%, and boosts peak lateral acceleration by 21.3% to 1.98 m/s², maintaining robust high-speed stability.
[2606.26313] Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
[Submitted on 24 Jun 2026]
Title:Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
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Abstract:This paper presents a hierarchical control framework using model predictive control (MPC) and reinforcement learning (RL) for active roll control to manage lateral load transfer during autonomous racing of a wheeled quadruped. The framework integrates offline time-optimal raceline generation, an online MPC planner that actively minimizes the lateral Load Transfer Ratio (LTR), and a low-level, whole-body RL policy deployed directly onto the robot's 16 actuators. The MPC is based on a vehicle dynamics bicycle model of the Unitree Go2-W platform. The robot's leg actuators act as active suspension where knee joints generate anti-roll torque to bank into turns. Physical track experiments demonstrate that active roll control reduces mean LTR by up to 44%, improves the fastest lap time by 8.7%, and boosts peak lateral acceleration capability by 21.3% to 1.98 $m/s^2$, maintaining robust high-speed stability beyond the range of a non-tilting baseline controller. Supplementary code and video can be found at this https URL
Comments: Accepted to the 17th International Symposium on Advanced Vehicle Control (AVEC 2026), 7-11 September 2026, Tsukuba, Japan
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2606.26313 [cs.RO]
(or arXiv:2606.26313v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.26313
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Marla Eisman [view email] [v1] Wed, 24 Jun 2026 18:54:17 UTC (966 KB)
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