Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning

Flexible manipulators such as tendon-driven ser-
pentine manipulators perform better than traditional rigid
ones in minimally invasive surgical tasks, including navigation
in confined space through key-hole like incisions. However, due
to the inherent nonlinearities and model uncertainties, motion
control of such manipulators becomes extremely challenging.
In this work, a hybrid framework combining Programming by
Demonstration (PbD) and reinforcement learning is proposed
to solve this problem. Gaussian Mixture Models (GMM),
Gaussian Mixture Regression (GMR) and linear regression are
used to learn the inverse kinematic model of the manipulator
from human demonstrations. The learned model is used as
nominal model to calculate the output end-effector trajectories
of the manipulator. Two surgical tasks are performed to
demonstrate the effectiveness of reinforcement learning: tube
insertion and circle following. Gaussian noise is introduced to
the standard model and the disturbed models are fed to the
manipulator to calculate the actuator input with respect to the
task specific end-effector trajectories. An expectation
maximization (E-M) based reinforcement learning algorithm is
used to update the disturbed model with returns from rollouts.
Simulation results have verified that the disturbed model can
be converged to the standard one and the tracking accuracy is
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