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This paper introduces a hardware-control co-design framework for quadrupedal robots with passive wheels to achieve efficient and versatile roller skating. The approach uses Bayesian Optimization to search the mechanical design space, coupled with Reinforcement Learning to train a motor control policy for each design candidate. The resulting co-designed robot outperforms human-engineered baselines and demonstrates advanced skating behaviors like hockey stops and self-alignment.
Quadrupedal robots can now skate circles around traditional designs, thanks to a co-design approach that unlocks dynamic maneuvers like hockey stops and self-alignment.
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.
