  Mobile soft robots offer compelling applications in fields ranging from urban
search and rescue to planetary exploration. A critical challenge of soft
robotic control is that the nonlinear dynamics imposed by soft materials often
result in complex behaviors that are counterintuitive and hard to model or
predict. As a consequence, most behaviors for mobile soft robots are discovered
through empirical trial and error and hand-tuning. A second challenge is that
soft materials are difficult to simulate with high fidelity -- leading to a
significant reality gap when trying to discover or optimize new behaviors. In
this work we employ a Quality Diversity Algorithm running model-free on a
physical soft tensegrity robot that autonomously generates a behavioral
repertoire with no a priori knowledge of the robot dynamics, and minimal human
intervention. The resulting behavior repertoire displays a diversity of unique
locomotive gaits useful for a variety of tasks. These results help provide a
road map for increasing the behavioral capabilities of mobile soft robots
through real-world automation.
