  Real-time proprioception is a challenging problem for soft robots, which have
almost infinite degrees-of-freedom in body deformation. When multiple actuators
are used, it becomes more difficult as deformation can also occur on actuators
caused by interaction between each other. To tackle this problem, we present a
method in this paper to sense and reconstruct 3D deformation on pneumatic soft
robots by first integrating multiple low-cost sensors inside the chambers of
pneumatic actuators and then using machine learning to convert the captured
signals into shape parameters of soft robots. An exterior motion capture system
is employed to generate the datasets for both training and testing. With the
help of good shape parameterization, the 3D shape of a soft robot can be
accurately reconstructed from signals obtained from multiple sensors. We
demonstrate the effectiveness of this approach on two designs of soft robots --
a robotic joint and a deformable membrane. After parameterizing the deformation
of these soft robots into compact shape parameters, we can effectively train
the neural networks to reconstruct the 3D deformation from the sensor signals.
The sensing and shape prediction pipeline can run at 50Hz in real-time on a
consumer-level device.
