Soft robots offer a variety of useful applications. However, the nature of their design makes them challenging to control using traditional techniques. Many applications therefore rely on machine learning-based methods, learning opaque control policies or dynamic models of the robot. This often results in overly complex and uninterpretable solutions for the problem at hand.
In this paper, a simplified approach is considered: Using a common feedforward neural network as a target adapter module, learned adaptations are added to the desired targets that are fed to a simple PID controller, essentially “deceiving” it into a better performance. This adapter is completely separate from the controller itself, allowing for relatively simple controllers to control and adapt to complex and evolving systems.
The approach is tested in simulation on a simple mass-springdamper control problem, where qualitative results show nearimmediate improvement in controller performance. The approach is then tested on a demonstrator origami robot, which relies for its functionality on the dynamics of the Kresling origami spring. Results show that this simple adaptation approach is robust to changes in controller tuning and changes in system dynamics. However, the method is sensitive to the sampling frequency at which training data is recorded.