We present a novel method for optimizing
and controlling soft robots while simultaneously
learning a compact representation
of the robot's state.  One of the largest challenges
in modeling, motion planning, and control of soft robots
is expressing their high-dimensional state space
with a lower-dimensional representation that
is tractable for control.
Existing methods such as modal analysis
can introduce modeling error and do not consider how
a task will be completed.
Our solution is to iteratively learn
a latent space while simultaneously optimizing
robot design parameters, control parameters, or
both.
We build upon recent work in differentiable
soft body physics engines which produce a fully differentiable
simulation for optimization
and differentiably handle contact.
Because these simulations are fully expressed on a grid,
We can apply deep, convolutional neural networks
to grid states and learn a latent space.
This control architecture is also fully differentiable,
meaning the entire system can be optimized
via gradient-based optimization techniques.
With our automated state representation, the only work
a human has to do is specify an initial robot morphology
and a loss function.
We demonstrate our algorithm on model robots.
This 2D biped must walk as far to the right as
possible.
Here's an optimized biped in both
materials and control.
Bluer regions represent stiffer material.
The center of the cross-marked section on this 2D arm
needs to reach the green circle. This arm is relatively
stiff compared to the amount of
actuation it can receive, and so it must optimize a controller
that can swing back and forth to build up velocity
to reach its target.
The center of the cross-marked section on this 2D "elephant"
needs to reach the green circle.
The elephant must optimize a controller so that it
can reach the target circle.
Here, we present saliency maps
demonstration how different actuations are influenced by the observed
latent variables.  The intensity of the colored squares
represents how much each actuator depends on the variable
over time.  The first four figures
correspond to leg actuators.  The latter six,
to trunk actuators.  As can be seen,
actuators in respective parts of the body have correlated dependence
on latent variables.
The second saliency map shows how output actuations
depend on pixels.  The actuators
tend to depend more on the extremal, and less on the internal
parts of the robot.  In this "bunny" task,
the robot must walk forward, and its two upper arms,
or "ears," must reach the two circles.
This is a challenge task that cannot be solved with 100%
accuracy.
This 2D rhinocerous robot was created directly
from a .png image to demonstrate
that our algorithm is capable of handling unstructured
inputs with curved sections.  In this extension to 3D,
this 3D quadruped must run as far
to the right as possible in the allotted time.
This variation presents a curved analog of
our boxier quadruped, and presents similar performance
in forward locomotion.
This hexapod,
which also must run as far to the right as possible,
represents our most dynamically complex
3D example with 24 actuators.
Thank you for your time.
