Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Chung-Pang Wang, Changwei Chen, Xiao Liang, Soofiyan Atar, Florian Richter, Michael C. Yip
AI summary
Problem
Autonomous surgical dissection systems lack reliable feedback to detect incomplete cuts or adapt to rapidly changing tissue states, leading to unpredictable failures and poor reliability in real-world environments.
Approach
The authors introduce a closed-loop framework that actively manipulates tissue to optimize camera visibility, estimates remaining tissue connectivity from endoscopic images, and automatically generates corrective dissection targets to ensure task completion.
Key results
- Novel exposure maximization controller that optimizes tissue deformation for optimal camera visibility
- Rule-based tissue connectivity estimation using tracked keypoint elongation ratios
- Greedy recovery planner that automatically generates corrective dissection targets for incomplete cuts
- Experimental validation on da Vinci Research Kit demonstrating improved autonomy and reduced errors across planning and learning-based methods
Why it matters
Enables safer, more reliable autonomous surgical systems by providing a practical feedback loop for real-time error detection and correction in dynamic tissue environments.
Abstract
Autonomous surgical systems must adapt to highly dynamic environments where tissue properties and visual cues evolve rapidly. Central to such adaptability is feedback: the ability to sense, interpret, and respond to changes during execution. While feedback mechanisms have been explored in surgical robotics, ranging from tool and tissue tracking to error detection, existing methods remain limited in handling the topological and perceptual challenges of tissue dissection. In this work, we propose a feedback-enabled framework for autonomous tissue dissection that explicitly reasons about topo- logical changes from endoscopic images after each dissection action. This structured feedback guides subsequent actions, enabling the system to localize dissection progress and adapt policies online. To improve the reliability of such feedback, we introduce visibility metrics that quantify tissue exposure and formulate optimal controller designs that actively manipulate tissue to maximize visibility. Finally, we integrate these feed- back mechanisms with both planning-based and learning-based dissection methods, and demonstrate experimentally that they significantly enhance autonomy, reduce errors, and improve robustness in complex surgical scenarios.