  Modeling and simulating soft robot hands can aid in design iteration for
complex and high degree-of-freedom (DoF) morphologies. This can be further
supplemented by iterating on the design based on its performance in real world
manipulation tasks. However, iterating in the real world requires an approach
that allows us to test new designs quickly at low costs. In this paper, we
leverage rapid prototyping of the hand using 3D-printing, and utilize
teleoperation to evaluate the hand in real world manipulation tasks. Using this
method, we design a 3D-printed 16-DoF dexterous anthropomorphic soft hand
(DASH) and iteratively improve its design over five iterations. Rapid
prototyping techniques such as 3D-printing allow us to directly evaluate the
fabricated hand without modeling it in simulation. We show that the design
improves over five design iterations through evaluating the hand's performance
in 30 real-world teleoperated manipulation tasks. Testing over 900
demonstrations shows that our final version of DASH can solve 19 of the 30
tasks compared to Allegro, a popular rigid hand in the market, which can only
solve 7 tasks. We open-source our CAD models as well as the teleoperated
dataset for further study.
