Learning-Based Adaptive Control for Surgical Robotic Exposure Task on Deformable Tissues

2026-05-18

AI Summary

This work addresses the challenge of autonomous tissue retraction during surgery, which is hindered by occlusions, geometric irregularities, and nonlinear biomechanical behavior. To overcome these issues, the authors propose an adaptive retraction control framework that integrates a deep deformation estimation model with a learning-based adaptive controller. Leveraging visual feedback, the system continuously optimizes its retraction strategy in real time. By employing sim-to-real transfer learning, the approach achieves zero-shot adaptation, enabling fully autonomous operation from initial tissue grasping to complete exposure of the target anatomical region—a milestone not previously demonstrated. Experimental validation on diverse deformable materials confirms the system’s efficacy and robustness, highlighting its strong potential for clinical translation.

Abstract

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.

Problem

• surgical robotic exposure
• deformable tissues
• tissue retraction
• autonomous control
• intraoperative visibility

Innovation

• learning-based adaptive control
• deformable tissue retraction
• deep deformation estimation
• zero-shot adaptation
• surgical robotic exposure