Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process -- fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.

翻译：演化虚拟生物是一个具有悠久历史的领域，近年来尤其受到软体机器人领域的关注。软材料的顺应性赋予了软体机器人复杂的行为，但也使其设计过程变得不直观，亟需自动化设计方法。尽管引起了广泛兴趣，但演化虚拟软体机器人的复杂性仍显不足，形态与控制的协同优化仍然是一个具有挑战性的问题。先前的研究识别并探讨了协同优化过程中的一个主要问题——大脑与身体的脆弱共同适应导致形态的早熟收敛。在本工作中，我们通过比较具有本体感知的可学习控制器与无任何观测的固定控制器（后者仅涉及形态优化），扩展了对这一现象的研究。我们在两个形态空间和两个复杂度不同的环境中进行的实验表明，形态空间中存在高表现区域的具体实例，这些区域在形态与控制的协同优化过程中无法被发现，但在单独优化形态时却存在且易于找到。因此，本工作清晰地展示并描述了协同优化中形态优化的挑战。基于这些结果，我们提出了一种以身体为中心的新框架来思考协同优化问题，这有助于从搜索视角理解该问题。我们希望通过本工作分享的见解能够引起对该问题的更多关注，并推动高效的大脑-身体协同优化的实现。