Modeling and Control
Modeling
Building on previous work on soft robotic arms, we develop computationally efficient physics models that capture only the relevant degrees of freedom of a soft robot. Our short-term goal is to pursue dual research tracks to achieve a universal modeling framework. For symmetric robots, we postulate that minimal parameter methods are sufficient to capture the dynamics and impedance of the deformable structures while being computationally efficient. For robots of irregular shape, we develop large scale finite element methods and make those models tractable by expanding on techniques such as model order reduction with state observers and deep learning methods. The creation of a modeling framework will be accelerated through active collaborations with faculty working on numerical simulations of physical systems, and researchers working on the modeling software external page SOFA, external page COMSOL, external page DiffPD.
Control
We conceive model-based controllers and reinforcement learning techniques that can perform difficult tasks requiring interaction with a robot’s surroundings. Our research was one of the first explorations into dynamic closed-loop control of soft continuum robots, with a short-term goal to generalize the current modeling approach to encapsulate non-linear deformations of hyperelastic materials in real-time. Collaborating with colleagues across faculty boundaries on developing new control strategies for soft robotic systems. The long-term goal is to use the model-based controllers as basic building blocks in a reinforcement learning framework for the development of advanced shapeshifting and autonomous manipulation capabilities for dexterous tasks.