Agreement Volatility: A Second-Order Metric for Uncertainty Quantification in Surgical Robot Learning

Jordan Thompson, Britton Jordan, Daniel S. Brown, Alan Kuntz

Keywords: Uncertainty Quantification, Uncertainty Attribution, Surgical Robotics

Abstract: Autonomous surgical robots are a promising solution to the increasing demand for surgery amid a shortage of surgeons. Recent work has proposed learning-based approaches for the autonomous manipulation of soft tissue. However, due to variability in aspects such as tissue geometries and stiffnesses, these methods do not always perform well, especially in out-of-distribution settings. To address this challenge, we propose a novel second-order metric for uncertainty quantification, agreement volatility, that enables successful and efficient collaborative handoffs between a human operator and a robot during soft-tissue manipulation by allowing the robot to know when to cede control to human operators and when to resume autonomous operation. We validate our approach using the daVinci Research Kit (dVRK) surgical robot to perform risk-aware physical soft-tissue manipulation. Our experimental results demonstrate that our proposed agreement volatility metric improves system success rates and leads to a 10\% lower reliance on human interventions compared to a variance-only baseline. We further demonstrate the usefulness of our agreement volatility metric as a spatial uncertainty map over geometric point cloud data, enabling uncertainty attribution which provides insight into regions of the input causing uncertainty.

Submission Number: 870