Bionic soft robots offer exciting perspectives for

more flexible and safe physical interaction with the world and

humans. Unfortunately, their hardware design often prevents

analytical modeling, which in turn is a prerequisite to apply

classical automatic control approaches. On the other hand,

also modeling by means of learning is hardly feasible due to

many degrees of freedom, high-dimensional state spaces and the

softness properties like e.g. mechanical elasticity, which cause

limited repeatability and complex dynamics. Nevertheless, the

realization of basic control modes is important to leverage the

potential of soft robots for applications. We therefore propose

a hybrid approach combining classical and learning elements

for the realization of an interactive control mode for an elastic

bionic robot. It superimposes a low-gain feedback control with a

feed-forward control based on a learned simplified model of the

inverse dynamics which considers only equilibria of the robot’s

dynamics. We demonstrate on the Bionic Handling Assistant

how a respective inverse equilibrium model can be learned and

effectively exploited for quick and agile control. In a second

step, the control scheme is extended to an active compliant

control mode. It implements a kind of gravitation compensation

to allow for kinesthetic teaching of the robot based on the

implicit knowledge of gravitational and mechanical forces that

are encoded in the learned equilibrium model.We finally discuss

that this control scheme may be implemented also on other

soft robots to provide the avenue towards their applications in

general manipulation tasks.