  Gait generation for soft robots is challenging due to the nonlinear dynamics
and high dimensional input spaces of soft actuators. Limitations in soft
robotic control and perception force researchers to hand-craft open loop
controllers for gait sequences, which is a non-trivial process. Moreover, short
soft actuator lifespans and natural variations in actuator behavior limit
machine learning techniques to settings that can be learned on the same time
scales as robot deployment. Lastly, simulation is not always possible, due to
heterogeneity and nonlinearity in soft robotic materials and their dynamics
change due to wear. We present a sample-efficient, simulation free, method for
self-generating soft robot gaits, using very minimal computation. This
technique is demonstrated on a motorized soft robotic quadruped that walks
using four legs constructed from 16 "handed shearing auxetic" (HSA) actuators.
To manage the dimension of the search space, gaits are composed of two
sequential sets of leg motions selected from 7 possible primitives. Pairs of
primitives are executed on one leg at a time; we then select the
best-performing pair to execute while moving on to subsequent legs. This method
-- which uses no simulation, sophisticated computation, or user input --
consistently generates good translation and rotation gaits in as low as 4
minutes of hardware experimentation, outperforming hand-crafted gaits. This is
the first demonstration of completely autonomous gait generation in a soft
robot.
