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Soft robotics aims to develop robots able to adapt their behavior across awide range of unstructured and unknown environments. A critical challenge ofsoft robotic control is that nonlinear dynamics often result in complexbehaviors hard to model and predict. Typically behaviors for mobile soft robotsare discovered through empirical trial and error and hand-tuning. Morerecently, optimization algorithms such as Genetic Algorithms (GA) have beenused to discover gaits, but these behaviors are often optimized for a singleenvironment or terrain, and can be brittle to unplanned changes to terrain. Inthis paper we demonstrate how Quality Diversity Algorithms, which search of arange of high-performing behaviors, can produce repertoires of gaits that arerobust to changing terrains. This robustness significantly out-performs that ofgaits produced by a single objective optimization algorithm.

Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits