  The nonlinearity and hysteresis of soft robot motions have posed challenges
in accurate soft robot control. Neural networks, especially recurrent neural
networks (RNNs), have been widely leveraged for this issue due to their
nonlinear activation functions and recurrent structures. Although they have
shown satisfying accuracy in most tasks, these black-box approaches are not
explainable, and hence, they are unsuitable for areas with high safety
requirements, like robot-assisted surgery. Based on the RNN controllers, we
propose a data-driven explainable controller (DDEC) whose parameters can be
updated online. We discuss the Jacobian controller and kinematics controller in
theory and demonstrate that they are only special cases of DDEC. Moreover, we
utilize RNN, the Jacobian controller, the kinematics controller, and DDECs for
trajectory following tasks. Experimental results have shown that our approach
outperforms the other controllers considering trajectory following errors while
being explainable. We also conduct a study to explore and explain the functions
of each DDEC component. This is the first interpretable soft robot controller
that overcomes the shortcomings of both NN controllers and interpretable
controllers. Future work may involve proposing different DDECs based on
different RNN controllers and exploiting them for high-safety-required
applications.
