  The Finite Element Method (FEM) is a powerful modeling tool for predicting
the behavior of soft robots. However, its use for control can be difficult for
non-specialists of numerical computation: it requires an optimization of the
computation to make it real-time. In this paper, we propose a learning-based
approach to obtain a compact but sufficiently rich mechanical representation.
Our choice is based on nonlinear compliance data in the actuator/effector space
provided by a condensation of the FEM model. We demonstrate that this compact
model can be learned with a reasonable amount of data and, at the same time, be
very efficient in terms of modeling, since we can deduce the direct and inverse
kinematics of the robot. We also show how to couple some models learned
individually in particular on an example of a gripper composed of two soft
fingers. Other results are shown by comparing the inverse model derived from
the full FEM model and the one from the compact learned version. This work
opens new perspectives, namely for the embedded control of soft robots, but
also for their design. These perspectives are also discussed in the paper.
