Biology provides many examples of how body adaption can be used to achieve a change in functionality. The feather star, an underwater crinoid that uses feather arms to locomote and feed, is one such system; it releases its arms to distract prey and vary its maneuverability to help escape predators. Using this crinoid as inspiration, we develop a robotic system that can alter its interaction with the environment by changing its morphology. We propose a robot that can actuate layers of flexible feathers and detach them at will. We first optimize the geometric and control parameters for a flexible feather using a hydrodynamic simulation followed by physical experiments. Second, we provide a theoretical framework for understanding how body change affects controllability. Third, we present a novel design of a soft swimming robot ( Figure 1 ) with the ability of changing its morphology. Using this optimized feather and theoretical framework, we demonstrate, on a robotic setup, how the detachment of feathers can be used to change the motion path while maintaining the same low-level controller.@en

The control possibilities for soft robots have long been hindered by the lack of accurate yet computationally treatable dynamic models of soft structures. Polynomial curvature models propose a solution to this quest for continuum slender structures. Nevertheless, the results produced with this class of models have been so far essentially theoretical. With the present work, we aim to provide a much-needed experimental validation to these recent theories. To this end, we focus on soft tentacles immersed in water. First, we propose an extension of the affine curvature model to underwater structures, considering the drag forces arising from the fluid-solid interaction. Then, we extensively test the model's capability to describe the system behavior across several shapes and working conditions. Finally, we validate model-based control policies, proposing and solving an optimal control problem for directional underwater swimming. Using the model we show an average increase of more than 3.5 times the swimming speed of a sinusoidal baseline controller, with some tentacles showing an improvement in excess of 5.5 times the baseline.@en

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.@en