In this paper we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy, employed as a gait learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where 'newborn' robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: How do gait learning algorithms compare when applied to various morphologies that are not known in advance (thus need to be treated without priors)? To answer this question, we use a test suite of twenty different robot morphologies to evaluate our gait learners and compare their efficiency, efficacy, and sensitivity to morphological differences. The results indicate that Bayesian Optimization and Differential Evolution deliver the same solution quality (walking speed for the robot) with fewer evaluations than the Evolution Strategy. Furthermore, the Evolution Strategy is more sensitive for morphological differences (its efficacy varies more between different morphologies) and is more subject to luck (repeated runs on the same morphology show greater variance in the outcomes).

中文翻译：

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比较可进化形态的机器人控制器优化方法。

在本文中，我们比较了贝叶斯优化、差分进化和进化策略，它们在模块化机器人中用作步态学习算法。动机场景是形态和控制器的联合进化，其中“新生”机器人也经历了一个学习过程来优化它们继承的控制器（不改变它们的身体）。这种情况提出了一个问题：步态学习算法在应用于事先不知道的各种形态时如何比较（因此需要在没有先验的情况下进行处理）？为了回答这个问题，我们使用包含 20 种不同机器人形态的测试套件来评估我们的步态学习器并比较它们的效率、功效和对形态差异的敏感性。结果表明，贝叶斯优化和差分进化提供了相同的解决方案质量（机器人的行走速度），评估次数少于进化策略。此外，进化策略对形态差异更敏感（其功效在不同形态之间差异更大）并且更受运气影响（在相同形态上重复运行显示结果差异更大）。