  In this paper, we study integrated estimation and control of soft robots. A
significant challenge in deploying closed loop controllers is reliable
proprioception via integrated sensing in soft robots. Despite the considerable
advances accomplished in fabrication, modelling, and model-based control of
soft robots, integrated sensing and estimation is still in its infancy. To that
end, this paper introduces a new method of estimating the degree of curvature
of a soft robot using a stretchable sensing skin. The skin is a spray-coated
piezoresistive sensing layer on a latex membrane. The mapping from the strain
signal to the degree of curvature is estimated by using a recurrent neural
network. We investigate uni-directional bending as well as bi-directional
bending of a single-segment soft robot. Moreover, an adaptive controller is
developed to track the degree of curvature of the soft robot in the presence of
dynamic uncertainties. Subsequently, using the integrated soft sensing skin, we
experimentally demonstrate successful curvature tracking control of the soft
robot.
