  Living organisms intertwine soft (e.g., muscle) and hard (e.g., bones)
materials, giving them an intrinsic flexibility and resiliency often lacking in
conventional rigid robots. The emerging field of soft robotics seeks to harness
these same properties in order to create resilient machines. The nature of soft
materials, however, presents considerable challenges to aspects of design,
construction, and control -- and up until now, the vast majority of gaits for
soft robots have been hand-designed through empirical trial-and-error. This
manuscript describes an easy-to-assemble tensegrity-based soft robot capable of
highly dynamic locomotive gaits and demonstrating structural and behavioral
resilience in the face of physical damage. Enabling this is the use of a
machine learning algorithm able to discover effective gaits with a minimal
number of physical trials. These results lend further credence to soft-robotic
approaches that seek to harness the interaction of complex material dynamics in
order to generate a wealth of dynamical behaviors.
