  Endowing continuum robots with compliance while it is interacting with the
internal environment of the human body is essential to prevent damage to the
robot and the surrounding tissues. Compared with passive compliance, active
compliance has the advantages in terms of increasing the force transmission
ability and improving safety with monitored force output. Previous studies have
demonstrated that active compliance can be achieved based on a complex model of
the mechanics combined with a traditional machine learning technique such as a
support vector machine. This paper proposes a recurrent neural network based
approach that avoids the complexity of modeling while capturing nonlinear
factors such as hysteresis, friction and delay of the electronics that are not
easy to model. The approach is tested on a 3-tendon single-segment continuum
robot with force sensors on each cable. Experiments are conducted to
demonstrate that the continuum robot with an RNN based feed-forward controller
is capable of responding to external forces quickly and entering an unknown
environment compliantly.
