用强化学习与贝叶斯优化协同设计四足滑轮机器人，实现高效多样的滑行运动。
Efficient and Versatile Quadrupedal Skating: Optimal Co-design via Reinforcement Learning and Bayesian Optimization
- 上下层优化：贝叶斯优化搜机械设计，强化学习训控制策略
- 速度更快、能耗更低，还能自动调向和急停刹车
- 首次实现四足滑行的系统级动态运动，适合机器人设计研究者
本文提出一种软硬件协同设计方法，使配备被动轮的四足机器人实现高效且多功能的滑行。被动轮可降低腿部惯性，提升高速下的能效。但无直接轮驱动导致机械设计与控制高度耦合。为此，我们构建双层优化框架：上层贝叶斯优化搜索机械设计空间，下层强化学习为每个候选设计训练电机控制策略。所获设计-策略组合不仅优于人工设计基准，还展现出如急停（侧向转向以最大化摩擦力）和自对齐运动（自动调整方向以提升前进效率）等多样化行为，这是首个针对四足机器人动态滑行运动的系统级研究。
原文摘要 · Abstract (English)
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.
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