Safe Bayesian Optimization

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Bayesian Retraction Optimization for Tissue Attachment Mapping in Surgical Dissection
Jul 21, 2026Shing-Hei Ho, Bao Thach, Toan Vo +2Safe Bayesian Optimization Surgical Robotics
The Robotics Center and the Kahlert School of Computing at the University of Utah, Salt Lake City, UT, USA · School of Computational Science and Engineering, Georgia Institute of Technology, GA, USA · Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University, TN, USA
Safe Bayesian Optimization with Counterfactual Policies
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College of Computer Science University of Massachusetts Amherst Amherst, MA 01002
Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows
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Jun 7, 2026Liqiu Dong, Marta Zagórowska, Mehmet MercangözBayesian Optimization Safe Bayesian Optimization
Department of Chemical Engineering, Imperial College London, London, UK · DCSC, Delft University of Technology, Delft, Netherlands
Local Preferential Bayesian Optimization
Jun 1, 2026Johanna Menn, Miriam Kober, Paul Brunzema +2Pairwise Preference Learning Pairwise Preference Evaluation
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany · Department of Clinical Research, University of Bern, Bern, Switzerland · Center for Reproducible Science and Research Synthesis, University of Zurich, Zurich, Switzerland +1
Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
May 18, 2026Changkun OuTool-Using Agents Safe Bayesian Optimization
Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
May 18, 2026Xinzhe Yuan, Zhuo Chen, Jianshu Zhang +4Bayesian Optimization Large Language Model-Guided Optimization
Shanghai Artificial Intelligence Laboratory, Shanghai, China · Shanghai Jiao Tong University, Shanghai, China · Harbin Institute of Technology, Harbin, China
Safe Bayesian Optimization for Uncertain Correlation Matrices in Linear Models of Co-Regionalization
May 13, 2026Jannis Lübsen, Annika EichlerBayesian Optimization Gaussian Processes
Institute of Control Systems, Hamburg University of Technology, Hamburg, Germany · Institute of Control Systems, Hamburg University of Technology · Deutsches Elektronen-Synchrotron, Hamburg, Germany
Regime-Conditioned Evaluation in Multi-Context Bayesian Optimization
May 6, 2026Noel ThomasCausal Effect Estimation Conditional Average Treatment Effect Estimation
Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, UAE
Safe learning-based control via function-based uncertainty quantification
Apr 1, 2026Abdullah Tokmak, Toni Karvonen, Thomas B. Schön +1Uncertainty Quantification Safety-Critical Control
Cyber-physical Systems Group, Aalto University, Espoo, Finland · School of Engineering Sciences, Lappeenranta–Lahti University of Technology LUT, Lappeenranta, Finland · Department of Information Technology, Uppsala University, Uppsala, Sweden

Jul 21, 2026, cs.ROJ/K move · Enter open · S save

Bayesian Retraction Optimization for Tissue Attachment Mapping in Surgical Dissection

Shing-Hei Ho, Bao Thach, Toan Vo, James M. Ferguson+1

The Robotics Center and the Kahlert School of Computing at the University of Utah, Salt Lake City, UT, USA · School of Computational Science and Engineering, Georgia Institute of Technology, GA, USA · Department of Electrical and Computer Engineering and Department of Computer Science, Vanderbilt University, TN, USA

Safe Bayesian Optimization Surgical Robotics

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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.

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