Learning to Control Reconfigurable Staged Soft Arms

In this work, we present a novel approach for modeling, and classifying between, the system load states introduced when constructing staged soft arm configurations. Through a two stage approach: (1) an LSTM calibration routine is used to identify the current load state then (2) a control input generation step combines a generalized quasistatic model with the learned load model. Our experiments show that accounting for system load allows us to more accurately control tapered arm configurations. We analyze the performance of our method using soft robotic actuators and show it is capable of classifying between different arm configurations at a rate greater than 95%. Additionally, our method is capable of reducing the end-effector error of quasistatic model only control to within 1 cm of our controller baseline.