Iterative Backpropagation Disturbance Observer with Forward Dynamics Model

Disturbance Observer (DOB) has been widely used for robotic applications to eliminate various kinds of disturbances. Recently, learning-based DOB has attracted significant attention as it can deal with complex robotic systems. In this study, we propose the Iterative Backpropagation Disturbance Observer (IB-DOB) method. IB-DOB learns the forward model with a neural network, and calculates disturbances via iterative backpropagations, which behaves like the inverse model. Our method can not only improve estimation performances owing to the iterative calculation but also be applied to both model-free and -based learning control. We conducted experiments for two manipulation tasks: the cart pole with Deep Deterministic Policy Gradient (DDPG) and the pushing object task with Deep Model Predictive Control (DeepMPC). Our method demonstrated better task performances than the baselines without DOB and with DOB using a learned inverse model even though disturbances of external forces and model errors were provided.