  In this paper, we present a synergic learning algorithm to address the task
of indirect manipulation of an unknown deformable tissue. Tissue manipulation
is a common yet challenging task in various surgical interventions, which makes
it a good candidate for robotic automation. We propose using a linear
approximate Q-learning method in which human knowledge contributes to selecting
useful yet simple features of tissue manipulation while the algorithm learns to
take optimal actions and accomplish the task. The algorithm is implemented and
evaluated on a simulation using the OpenCV and CHAI3D libraries. Successful
simulation results for four different configurations which are based on
realistic tissue manipulation scenarios are presented. Results indicate that
with a careful selection of relatively simple and intuitive features, the
developed Q-learning algorithm can successfully learn an optimal policy without
any prior knowledge of tissue dynamics or camera intrinsic/extrinsic
calibration parameters.
