Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
For autonomous racing of wheeled quadrupeds, this work provides a method to improve stability and speed through active load transfer mitigation.
This paper presents a hierarchical control framework combining MPC and RL for active roll control in wheeled quadruped racing, reducing lateral load transfer by up to 44%, improving lap time by 8.7%, and increasing peak lateral acceleration by 21.3% to 1.98 m/s².
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 https://github.com/meisman-ucb/go2w-roll-control-mpc