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The success of soft robots in displaying emergent behaviors is tightly linkedto the compliant interaction with the environment. However, to exploit suchphenomena, proprioceptive sensing methods which do not hinder their softnessare needed. In this work we propose a new sensing approach for soft underwaterslender structures based on embedded pressure sensors and use a learning-basedpipeline to link the sensor readings to the shape of the soft structure. Usingtwo different modeling techniques, we compare the pose reconstruction accuracyand identify the optimal approach. Using the proprioceptive sensingcapabilities we show how this information can be used to assess the swimmingperformance over a number of metrics, namely swimming thrust, tip deflection,and the traveling wave index. We conclude by demonstrating the robustness ofthe embedded sensor on a free swimming soft robotic squid swimming at a maximumvelocity of 9.5 cm/s, with the absolute tip deflection being predicted withinan error less than 9% without the aid of external sensors.

Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction for Controller Optimization