  The success of soft robots in displaying emergent behaviors is tightly linked
to the compliant interaction with the environment. However, to exploit such
phenomena, proprioceptive sensing methods which do not hinder their softness
are needed. In this work we propose a new sensing approach for soft underwater
slender structures based on embedded pressure sensors and use a learning-based
pipeline to link the sensor readings to the shape of the soft structure. Using
two different modeling techniques, we compare the pose reconstruction accuracy
and identify the optimal approach. Using the proprioceptive sensing
capabilities we show how this information can be used to assess the swimming
performance over a number of metrics, namely swimming thrust, tip deflection,
and the traveling wave index. We conclude by demonstrating the robustness of
the embedded sensor on a free swimming soft robotic squid swimming at a maximum
velocity of 9.5 cm/s, with the absolute tip deflection being predicted within
an error less than 9% without the aid of external sensors.

Robotics