# Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization

arXiv: https://arxiv.org/abs/2603.18408 · HTML: https://mlsanity.com/paper/2603.18408/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 19 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Sim-to-Real: Learning Agile Locomotion For Quadruped Robots](https://mlsanity.com/paper/1804.10332/) (2018)
- [Reinforcement Learning for Improving Agent Design](https://mlsanity.com/paper/1810.03779/) (2018)
- [Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.01432/) (2018)
- [GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots](https://mlsanity.com/paper/2209.05309/) (2022)
- [Learning-based legged locomotion; state of the art and future perspectives](https://mlsanity.com/paper/2406.01152/) (2024)
- [LocoFormer: Generalist Locomotion via Long-context Adaptation](https://mlsanity.com/paper/2509.23745/) (2025)
- [Meta Reinforcement Learning for Optimal Design of Legged Robots](https://mlsanity.com/paper/2210.02750/) (2022)
- [Learning-based Design and Control for Quadrupedal Robots with Parallel-Elastic Actuators](https://mlsanity.com/paper/2301.03509/) (2023)
- [Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots](https://mlsanity.com/paper/1809.03557/) (2018)
- [CTBC: Contact-Triggered Blind Climbing for Wheeled Bipedal Robots with Instruction Learning and Reinforcement Learning](https://mlsanity.com/paper/2509.02986/) (2025)

## Built on by (most cited first, 2 of 2)

- [Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization](https://mlsanity.com/paper/2610.02366/) (2026)
- [Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads](https://mlsanity.com/paper/2610.04238/) (2026)

## Abstract (arXiv)

In this paper, we present a hardware-control co-design approach that enables efficient and versatile roller skating on quadrupedal robots equipped with passive wheels. Passive-wheel skating reduces leg inertia and improves energy efficiency, particularly at high speeds. However, the absence of direct wheel actuation tightly couples mechanical design and control. To unlock the full potential of this modality, we formulate a bilevel optimization framework: an upper-level Bayesian Optimization searches the mechanical design space, while a lower-level Reinforcement Learning trains a motor control policy for each candidate design. The resulting design-policy pairs not only outperform human-engineered baselines, but also exhibit versatile behaviors such as hockey stop (rapid braking by turning sideways to maximize friction) and self-aligning motion (automatic reorientation to improve energy efficiency in the direction of travel), offering the first system-level study of dynamic skating motion on quadrupedal robots.