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In this paper, we present a synergic learning algorithm to address the taskof indirect manipulation of an unknown deformable tissue. Tissue manipulationis a common yet challenging task in various surgical interventions, which makesit a good candidate for robotic automation. We propose using a linearapproximate Q-learning method in which human knowledge contributes to selectinguseful yet simple features of tissue manipulation while the algorithm learns totake optimal actions and accomplish the task. The algorithm is implemented andevaluated on a simulation using the OpenCV and CHAI3D libraries. Successfulsimulation results for four different configurations which are based onrealistic tissue manipulation scenarios are presented. Results indicate thatwith a careful selection of relatively simple and intuitive features, thedeveloped Q-learning algorithm can successfully learn an optimal policy withoutany prior knowledge of tissue dynamics or camera intrinsic/extrinsiccalibration parameters.

Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach