Toward Synergic Learning for Autonomous Manipulation of Deformable Tissues via Surgical Robots: An Approximate Q-Learning Approach
This work addresses a challenging task in surgical robotics for automating tissue manipulation, but it is incremental as it builds on existing Q-learning methods with human-guided feature selection.
The paper tackled the problem of robotic manipulation of unknown deformable tissues in surgery by developing a synergic learning algorithm using approximate Q-learning, which successfully learned optimal policies in simulations across four realistic scenarios without prior knowledge of tissue dynamics or camera parameters.
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.