  The manual design of soft robots and their controllers is notoriously
challenging, but it could be augmented---or, in some cases, entirely
replaced---by automated design tools. Machine learning algorithms can
automatically propose, test, and refine designs in simulation, and the most
promising ones can then be manufactured in reality (sim2real). However, it is
currently not known how to guarantee that behavior generated in simulation can
be preserved when deployed in reality. Although many previous studies have
devised training protocols that facilitate sim2real transfer of control
polices, little to no work has investigated the simulation-reality gap as a
function of morphology. This is due in part to an overall lack of tools capable
of systematically designing and rapidly manufacturing robots. Here we introduce
a low cost, open source, and modular soft robot design and construction kit,
and use it to simulate, fabricate, and measure the simulation-reality gap of
minimally complex yet soft, locomoting machines. We prove the scalability of
this approach by transferring an order of magnitude more robot designs from
simulation to reality than any other method. The kit and its instructions can
be found here: https://github.com/skriegman/sim2real4designs
