Improving model-based control of a soft robot via gaussian process regression

Abstract

Soft robots have the potential to accelerate robotiza- tion in areas that are complex and impractical for hard robots. The use of soft materials results in a safe and flexible design that is unattainable for hard robots. However, this attribute results in the need for new control approaches and strategies. Hybrid controllers are a relative unexplored type of controllers that consist of a model-based controller part and a learning part to correct the model-based controller. A hybrid controller benefit by the unrequired need for accurate system identification. Simultaneously, the learning effort is reduced by the preliminary work of the model-based component. In this project, a model-based feedforward controller is pro- posed and compared with a hybrid controller consisting of the same model-based controller enhanced with a Gaussian process to reduce the end-point error in the bending angle. The controllers are tested using a crafted 2-segment pneumatic silicone soft robot, following a circular trajectory with different radii. The results of this new control strategy highlights the poten- tial benefits of adding a learning approach to a model-based controller to reduce model errors. Using a relative small dataset preserves a computational usable Gaussian process. The small dataset remains effective by reducing the range of the training data.