  Tensegrity robots, composed of rigid rods and flexible cables, exhibit high
strength-to-weight ratios and significant deformations, which enable them to
navigate unstructured terrains and survive harsh impacts. They are hard to
control, however, due to high dimensionality, complex dynamics, and a coupled
architecture. Physics-based simulation is a promising avenue for developing
locomotion policies that can be transferred to real robots. Nevertheless,
modeling tensegrity robots is a complex task due to a substantial sim2real gap.
To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy
for tensegrity robots. This strategy is based on a differentiable physics
engine that can be trained given limited data from a real robot. These data
include offline measurements of physical properties, such as mass and geometry
for various robot components, and the observation of a trajectory using a
random control policy. With the data from the real robot, the engine can be
iteratively refined and used to discover locomotion policies that are directly
transferable to the real robot. Beyond the R2S2R pipeline, key contributions of
this work include computing non-zero gradients at contact points, a loss
function for matching tensegrity locomotion gaits, and a trajectory
segmentation technique that avoids conflicts in gradient evaluation during
training. Multiple iterations of the R2S2R process are demonstrated and
evaluated on a real 3-bar tensegrity robot.
