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

翻译：软体机器人学旨在开发能够在各种非结构化和未知环境中自适应行为的机器人。软体机器人控制的一个关键挑战在于非线性动力学通常导致难以建模和预测的复杂行为。通常，移动软体机器人的行为通过经验性的试错法和手动调参来发现。最近，遗传算法等优化算法已被用于发现步态，但这些行为往往针对单一环境或地形进行优化，并且在地形发生意外变化时可能表现脆弱。本文展示了质量多样性算法（通过搜索一系列高性能行为）如何能够生成对地形变化具有鲁棒性的步态库。这种鲁棒性显著优于单一目标优化算法所产生的步态。