  Traditional dynamic models of continuum robots are in general computationally
expensive and not suitable for real-time control. Recent approaches using
learning-based methods to approximate the dynamic model of continuum robots for
control have been promising, although real data hungry -- which may cause
potential damage to robots and be time consuming -- and getting poorer
performance when trained with simulation data only. This paper presents a
model-based learning framework for continuum robot closed-loop control that, by
combining simulation and real data, shows to require only 100 real data to
outperform a real-data-only controller trained using up to 10000 points. The
introduced data-efficient framework with three control policies has utilized a
Gaussian process regression (GPR) and a recurrent neural network (RNN). Control
policy A uses a GPR model and a RNN trained in simulation to optimize control
outputs for simulated targets; control policy B retrains the RNN in policy A
with data generated from the GPR model to adapt to real robot physics; control
policy C utilizes policy A and B to form a hybrid policy. Using a continuum
robot with soft spines, we show that our approach provides an efficient
framework to bridge the sim-to-real gap in model-based learning for continuum
robots.
