Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
For autonomous surgical systems, this work addresses the challenge of adapting to dynamic tissue environments by integrating explicit topological and visual feedback, though the improvements are incremental over existing methods.
The paper proposes a feedback-enabled framework for autonomous tissue dissection that uses topological and visual feedback from endoscopic images to guide actions, improving autonomy, reducing errors, and enhancing robustness in complex surgical scenarios.
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 topological 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 feedback 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.