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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.

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Although often regarded a childhood toy, the design of paper airplanes is subtly complex. The design space and mapping from geometry to distance flown is highly nonlinear and probabilistic where a single airplane design exhibits a multitude of trajectory forms and flight distances. This makes optimization and understanding of their behavior challenging for humans. By understanding the behavior of paper airplanes and predicting flight behavior, there is a potential to improve the design of aerial vehicles that operate at low Reynolds numbers. By developing a robotic system that can fabricate, test, analyze, and model the flight behavior in an unsupervised fashion, a wide design space can be reliably characterized. We find there are discrete behavioral groups that result in different trajectories: nose dive, glide, and recovery glide. Informed by this characterization we propose a method of using Gaussian mixture models to extract the clusters of the design space that map to these different behaviors. This allows us to solve both the forward and reverse design problem for paper airplanes, and also to perform efficient optimization of the geometry for a given target flight distance.

The morphology and structure plays a crucial role in shaping the behavior and functionality of both biological and robotic systems.In this work, we are inspired by morphogenesis, a fundamental biological process which encompasses the emergence of organs and organism morphology. It is driven by both internal and environmental factors and profoundly influences cellular behavior during development. By studying the environmental-driven properties that give rise to specific cellular morphologies, we investigate how control of the environment can be harnessed to trigger structural changes. Furthermore, we explore the potential application of these principles to inspire the development of robots with predefined morphologies. However, this endeavor is inherently stochastic and challenging to simulate accurately. To address this complexity, we propose leveraging a Markov decision process-inspired controller, guided by a Markov model. Using this approach, we can achieve decentralized control instead of localized control and design scalable robotic systems that can reconfigure their morphologies and display different motion characteristics in response to environmental cues.