While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and accelerate the development of new breeds of soft robots, a comprehensive virtual platform with well-established tasks, environments, and evaluation metrics is needed. In this work, we introduce SoftZoo, a soft robot co-design platform for locomotion in diverse environments. SoftZoo supports an extensive, naturally-inspired material set, including the ability to simulate environments such as flat ground, desert, wetland, clay, ice, snow, shallow water, and ocean. Further, it provides a variety of tasks relevant for soft robotics, including fast locomotion, agile turning, and path following, as well as differentiable design representations for morphology and control. Combined, these elements form a feature-rich platform for analysis and development of soft robot co-design algorithms. We benchmark prevalent representations and co-design algorithms, and shed light on 1) the interplay between environment, morphology, and behavior; 2) the importance of design space representations; 3) the ambiguity in muscle formation and controller synthesis; and 4) the value of differentiable physics. We envision that SoftZoo will serve as a standard platform and template an approach toward the development of novel representations and algorithms for co-designing soft robots' behavioral and morphological intelligence.

翻译：尽管机器人学习在控制领域取得了显著研究进展，但在同时协同优化形态结构时仍面临独特挑战。现有工作通常针对特定环境或表征方法设计。为更充分理解固有设计与性能的权衡关系，并加速新型软体机器人的开发，亟需构建包含完善任务、环境和评估指标的综合虚拟平台。本研究提出SoftZoo——面向多样环境运动的软体机器人协同设计平台。SoftZoo支持广泛且受自然启发的材料库，可模拟平地、沙漠、湿地、黏土、冰面、雪地、浅水和海洋等环境。此外，该平台提供软体机器人相关的多样化任务，包括快速运动、敏捷转向和路径跟踪，以及用于形态与控制的可微分设计表征。这些要素共同构成分析开发软体机器人协同设计算法的功能丰富平台。我们对主流表征方法与协同设计算法进行基准测试，揭示了：1）环境-形态-行为间的相互作用；2）设计空间表征的重要性；3）肌肉形成与控制器综合的模糊性；4）可微分物理的价值。我们预期SoftZoo将成为标准平台，为开发协同设计软体机器人行为与形态智能的新型表征与算法提供模板化方法。