# Safe Uncertainty-Aware Learning of Robotic Suturing

arXiv: https://arxiv.org/abs/2505.16596 · HTML: https://mlsanity.com/paper/2505.16596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 68 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Implicit Behavioral Cloning](https://mlsanity.com/paper/2109.00137/) (2021)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks](https://mlsanity.com/paper/1903.08792/) (2019)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1910.02923/) (2019)
- [Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks](https://mlsanity.com/paper/2407.12998/) (2024)
- [A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods](https://mlsanity.com/paper/2310.06873/) (2023)
- [MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities](https://mlsanity.com/paper/2405.17419/) (2024)
- [Adaptive Safety with Control Barrier Functions](https://mlsanity.com/paper/1910.00555/) (2019)
- [From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence](https://mlsanity.com/paper/2501.03282/) (2025)
- [ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity](https://mlsanity.com/paper/2404.16027/) (2024)
- [Input-to-State Safety With Control Barrier Functions](https://mlsanity.com/paper/1803.03035/) (2018)
- [Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics](https://mlsanity.com/paper/2011.07183/) (2020)
- [Model-Free Safety-Critical Control for Robotic Systems](https://mlsanity.com/paper/2109.09047/) (2021)
- [Safety-Critical Kinematic Control of Robotic Systems](https://mlsanity.com/paper/2009.09100/) (2020)
- [COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling](https://mlsanity.com/paper/2209.06424/) (2022)
- [SurgicAI: A Hierarchical Platform for Fine-Grained Surgical Policy Learning and Benchmarking](https://mlsanity.com/paper/2406.13865/) (2024)
- [Model Free Barrier Functions via Implicit Evading Maneuvers](https://mlsanity.com/paper/2107.12871/) (2021)
- [Robust Safe Control Synthesis with Disturbance Observer-Based Control Barrier Functions](https://mlsanity.com/paper/2201.05758/) (2022)
- [Online scalable Gaussian processes with conformal prediction for guaranteed coverage](https://mlsanity.com/paper/2410.05444/) (2024)
- [Observer-based Control Barrier Functions for Safety Critical Systems](https://mlsanity.com/paper/2110.00923/) (2021)
- [Robust Adaptive Safe Robotic Grasping with Tactile Sensing](https://mlsanity.com/paper/2411.07833/) (2024)
- [Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification](https://mlsanity.com/paper/2501.10561/) (2025)
- [Towards Uncertainty Unification: A Case Study for Preference Learning](https://mlsanity.com/paper/2503.19317/) (2025)
- [Towards Robotic Laboratory Automation Plug & Play: The "LAPP" Framework](https://mlsanity.com/paper/2106.10129/) (2021)
- [Barrier functions enable safety-conscious force-feedback control](https://mlsanity.com/paper/2209.12270/) (2022)

## Built on by (most cited first, 1 of 1)

- [Triggering Generalist Reasoning via Predictive Uncertainty for Dual-System VLA](https://mlsanity.com/paper/2610.05025/) (2026)

## Abstract (arXiv)

Robot-Assisted Minimally Invasive Surgery is currently fully manually controlled by a trained surgeon. Automating this has great potential for alleviating issues, e.g., physical strain, highly repetitive tasks, and shortages of trained surgeons. For these reasons, recent works have utilized Artificial Intelligence methods, which show promising adaptability. Despite these advances, there is skepticism of these methods because they lack explainability and robust safety guarantees. This paper presents a framework for a safe, uncertainty-aware learning method. We train an Ensemble Model of Diffusion Policies using expert demonstrations of needle insertion. Using an Ensemble model, we can quantify the policy's epistemic uncertainty, which is used to determine Out-Of-Distribution scenarios. This allows the system to release control back to the surgeon in the event of an unsafe scenario. Additionally, we implement a model-free Control Barrier Function to place formal safety guarantees on the predicted action. We experimentally evaluate our proposed framework using a state-of-the-art robotic suturing simulator. We evaluate multiple scenarios, such as dropping the needle, moving the camera, and moving the phantom. The learned policy is robust to these perturbations, showing corrective behaviors and generalization, and it is possible to detect Out-Of-Distribution scenarios. We further demonstrate that the Control Barrier Function successfully limits the action to remain within our specified safety set in the case of unsafe predictions.