Soft robots are challenging to model due to their nonlinear behavior. However, their soft bodies make it possible to safely observe their behavior under random control inputs, making them amenable to large-scale data collection and system identification. Koopman Operator Theory offers a way to represent a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions called observables, enabling models of nonlinear systems to be constructed via linear regression of observed data. I have used this method to construct a dynamic model of a pneumatic soft robot and predict its behavior. [video]
Controlling soft robots with precision is a challenge due in large part to the difficulty of constructing models that are amenable to model-based control design techniques. Koopman Operator Theory offers a way to construct explicit linear dynamical models of soft robots and to control them using established model-based linear control methods. This method is data-driven, yet unlike other data-driven models such as neural networks, it yields an explicit control-oriented linear model rather than just a "black-box" input-output mapping. I have used this method to control a soft pneumatic robot arm. [video]
Robotics offers biologists a powerful tool for experimentally evaluating evolutionary hypotheses. Robots that are designed to mimic specific biological traits allow the fitness of those traits to be assessed via controlled experiments, and can even be used to test the fitness of theoretical and extinct traits. In collaboration with the University of Michigan's department of ecology and evolutionary biology, I am developing biomimetic snake robots in an effort to better understand the adaptations snakes use to deter predators.