  Even though artificial muscles have gained popularity due to their compliant,
flexible, and compact properties, there currently does not exist an easy way of
making informed decisions on the appropriate actuation strategy when designing
a muscle-powered robot; thus limiting the transition of such technologies into
broader applications. What's more, when a new muscle actuation technology is
developed, it is difficult to compare it against existing robot muscles. To
accelerate the development of artificial muscle applications, we propose a data
driven approach for robot muscle actuator selection using Support Vector
Machines (SVM). This first-of-its-kind method gives users gives users insight
into which actuators fit their specific needs and actuation performance
criteria, making it possible for researchers and engineer with little to no
prior knowledge of artificial muscles to focus on application design. It also
provides a platform to benchmark existing, new, or yet-to-be-discovered
artificial muscle technologies. We test our method on unseen existing robot
muscle designs to prove its usability on real-world applications. We provide an
open-access, web-searchable interface for easy access to our models that will
additionally allow for continuous contribution of new actuator data from groups
around the world to enhance and expand these models.
