发表机构
The Robotics Center and the Kahlert School of Computing at the University of Utah; School of Computational Science and Engineering, Georgia Institute of Technology; Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University（美国犹他大学机器人中心和卡尔特计算学院; 美国佐治亚理工学院计算科学与工程学院; 美国范德堡大学电气与计算机工程系和计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对外科医生短缺，该研究将组织附着识别视为概率问题，提出基于顺序贝叶斯希尔伯特地图和贝叶斯回缩优化的方法，避免显式组织建模，在模拟中验证并实现向实际机器人解剖实验的零样本转移，为自动化手术解剖提供新途径。
AI 中文摘要
随着外科医生短缺加剧，自动化手术子任务（如组织解剖）有望减轻工作量并扩大患者就医机会。以往工作依赖无法量化不确定性的手工切口策略或基于强建模假设的模拟方法。我们将组织附着识别视为概率问题，提出贝叶斯方法，避免显式组织建模。该方法用顺序贝叶斯希尔伯特地图表示组织点附着于切除表面的可能性，通过学习的分类器集合从机器人组织回缩获取的空间数据预测附着可能性以更新地图。为规划下一步回缩，设计贝叶斯回缩优化在安全约束下选择最具信息性的动作。随着地图细化，高附着可能性区域被选择性切开。我们在模拟中验证了该方法，并展示了向实际机器人解剖实验的零样本转移。
英文摘要
With growing surgeon shortages, automating surgical sub-tasks such as tissue dissection offers a promising step toward reducing workload and expanding patient access. Prior work has relied on hand-crafted incision policies that cannot quantify uncertainty or has relied on simulation-based methods that require strong modeling assumptions. We instead view tissue attachment identification as an inherently probabilistic problem and propose a Bayesian approach that avoids explicit tissue modeling. Our method uses a Sequential Bayesian Hilbert Map (SBHM) to represent the likelihood that each tissue point is attached to the underlying resection surface. An ensemble of learned classifiers predicts attachment likelihoods from spatial data acquired during robotic tissue retraction, with each classifier serving as a noisy information source to update the SBHM. To plan the next retraction, we devise Bayesian Retraction Optimization (BRO) to select the most informative action under safety constraints. As the SBHM refines over time, regions with high attachment likelihood are selectively incised. We validate our method in simulation across diverse tissue geometries and acquisition strategies, and demonstrate zero-shot transfer to real robotic dissection experiments.
CommentsIEEE IROS 2026 preprint
