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SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments

2023-03-16 17:59:50
Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan


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.

Abstract (translated)

在机器人学习控制方面已经取得了显著的研究进展,但同时当同时优化形态学时,也带来了独特的挑战。现有的工作通常针对特定的环境和表示进行定制。为了更深入地理解固有的设计和性能权衡以及加速软机器人新种类的发展,需要建立一个全面的任务、环境和评估指标完善的虚拟平台。在本研究中,我们介绍了SoftZoo,一个软机器人在多种环境中 Locomotion 的联合设计平台。SoftZoo 支持广泛的自然启发材料集,包括能够模拟平坦地面、沙漠、湿地、黏土、冰、雪、浅水以及海洋的环境。此外,它提供了与软机器人相关的多种任务,包括快速行走、敏捷转身和路径跟随,以及形态和控制的可区分设计表示。将它们综合起来,组成了一个重要的分析和发展软机器人联合设计算法的平台。我们基准了现有的表示和联合设计算法,并揭示了 1) 环境、形态和行为之间的交互作用;2) 设计空间表示的重要性;3) 肌肉形成和控制器合成的歧义;以及4) 可区分物理学的价值。我们预计SoftZoo将成为开发新表示和算法,以联合设计软机器人的行为和形态智能的标准平台和模板。



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