  Soft robots show compliance and have infinite degrees of freedom. Thanks to
these properties, such robots can be leveraged for surgery, rehabilitation,
biomimetics, unstructured environment exploring, and industrial grippers. In
this case, they attract scholars from a variety of areas. However, nonlinearity
and hysteresis effects also bring a burden to robot modeling. Moreover,
following their flexibility and adaptation, soft robot control is more
challenging than rigid robot control. In order to model and control soft
robots, a large number of data-driven methods are utilized in pairs or
separately. This review first briefly introduces two foundations for
data-driven approaches, which are physical models and the Jacobian matrix, then
summarizes three kinds of data-driven approaches, which are statistical method,
neural network, and reinforcement learning. This review compares the modeling
and controller features, e.g., model dynamics, data requirement, and target
task, within and among these categories. Finally, we summarize the features of
each method. A discussion about the advantages and limitations of the existing
modeling and control approaches is presented, and we forecast the future of
data-driven approaches in soft robots. A website
(https://sites.google.com/view/23zcb) is built for this review and will be
updated frequently.
