Learning-Based Adaptive Control for Surgical Robotic Exposure Task on Deformable Tissues
This work addresses the challenge of autonomous tissue retraction for surgical robots, a critical task for exposing occluded regions during minimally invasive procedures.
The paper proposes a learning-based adaptive control framework for autonomous tissue retraction in surgery, achieving zero-shot adaptation and full ROI exposure in simulations and real-world experiments on deformable materials.
In various surgical procedures, regions of interest (ROIs) such as organs or lesions are often occluded by overlying tissues, requiring surgeons to achieve adequate exposure for precise intervention. However, the irregular geometry, nonlinear biomechanical properties of overlying tissues, and limited intraoperative visibility of the ROI pose significant challenges to the autonomous execution of tissue retraction. To address this, we formulate a realistic model of the tissue retraction task and propose a learning-based adaptive control framework for achieving ROI exposure. The method optimizes control inputs online by monitoring changes in the visual boundary of the tissue, while leveraging a deep deformation estimation model trained on simulation data to identify the optimal grasping point and ensure the convergence and safety of the adaptive controller. Through simulations and real-world experiments on different deformable materials, it has been demonstrated that this framework exhibits zero-shot adaptation to similar tasks and can complete the autonomous retraction process, from initial grasp selection to full ROI exposure. Therefore, it has the potential to be applied in actual surgical assistance scenarios.