# Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

arXiv: https://arxiv.org/abs/1809.03557 · HTML: https://mlsanity.com/paper/1809.03557/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots](https://mlsanity.com/paper/2402.06143/) (2024)
- [Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization](https://mlsanity.com/paper/2603.18408/) (2026)
- [ATRos: Learning Energy-Efficient Agile Locomotion for Wheeled-legged Robots](https://mlsanity.com/paper/2510.09980/) (2025)
- [Energy-Efficient Omnidirectional Locomotion for Wheeled Quadrupeds via Predictive Energy-Aware Nominal Gait Selection](https://mlsanity.com/paper/2601.10723/) (2025)
- [Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning](https://mlsanity.com/paper/2606.21387/) (2026)
- [Racing a Wheeled Quadruped: Active Load Transfer Mitigation via Model Predictive Control](https://mlsanity.com/paper/2606.26313/) (2026)
- [Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot](https://mlsanity.com/paper/2606.31807/) (2026)

## Abstract (arXiv)

We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.