  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.
