Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
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/s2, 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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 17 references that are in the map. First posted on arXiv 24 Jun 2026.
Builds on
3 of 17 references.
References · 3
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2018Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robotscited by 8
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- 2025On learning racing policies with reinforcement learning
- 2024Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach
- 2026Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization
- 2022TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach
- 2024Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
- 2023A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
- 2025Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
- 2025Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing