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Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery

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Efficient and Accurate Mapping of Subsurface Anatomy via Onl...

IEEE International Conference on robotics and Automation (ICRA)

作者： Brian Y. Cho Alan Kuntz Robotics Center and Kahlert School of Computing University of Utah Salt Lake City UT USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, A * search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy.

关键词： Accuracy Three-dimensional displays Medical robotics Costs Computed tomography Robot sensing systems Probabilistic logic

Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery

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Reward Learning from Suboptimal Demonstrations with Applicat...

International Symposium on Medical robotics (ISMR)

作者： Zohre Karimi Shing-Hei Ho Bao Thach Alan Kuntz Daniel S. Brown Robotics Center and the Kahlert School of Computing at the University of Utah Salt Lake City UT USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Automating robotic surgery via learning from demonstration (LfD) techniques is extremely challenging. This is because surgical tasks often involve sequential decision-making processes with complex interactions of physical objects and have low tolerance for mistakes. Prior works assume that all demonstrations are fully observable and optimal, which might not be practical in the real world. This paper introduces a sample-efficient method that learns a robust reward function from a limited amount of ranked suboptimal demonstrations consisting of partial-view point cloud observations. The method then learns a policy by optimizing the learned reward function using reinforcement learning (RL). We show that using a learned reward function to obtain a policy is more robust than pure imitation learning. We apply our approach on a physical surgical electrocautery task and demonstrate that our method can perform well even when the provided demonstrations are suboptimal and the observations are high-dimensional point clouds. Code and videos available here: https://***/viewllfdinelectrocautery

关键词： Point cloud compression Medical robotics Codes Imitation learning Decision making Surgery Reinforcement learning

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks

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Modeling Kinematic Uncertainty of Tendon-Driven Continuum Ro...

International Symposium on Medical robotics (ISMR)

作者： Jordan Thompson Brian Y. Cho Daniel S. Brown Alan Kuntz Robotics Center and the Kahlert School of Computing at the University of Utah Salt Lake City UT USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate one use of this model through a trajectory optimization method that explicitly reasons about the workspace uncertainty to minimize the probability of collision.

关键词： Geometry Uncertainty Computational modeling Process control Kinematics Planning Continuum robots

Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery

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arXiv 2024年

作者： Karimi, Zohre Ho, Shing-Hei Thach, Bao Kuntz, Alan Brown, Daniel S. Robotics Center The Kahlert School of Computing The University of Utah Salt Lake CityUT84112 United States

关键词： Robotic surgery

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks

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arXiv 2024年

作者： Thompson, Jordan Cho, Brian Y. Brown, Daniel S. Kuntz, Alan The Robotics Center the Kahlert School of Computing The University of Utah Salt Lake CityUT84112 United States

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot’s operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate one use of this model through a trajectory optimization method that explicitly reasons about the workspace uncertainty to minimize the probability of collision. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification

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arXiv 2025年

作者： Thompson, Jordan Koe, Ronald Le, Anthony Goodman, Gabriella Brown, Daniel S. Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Biomedical Engineering University of Utah United States

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 tissue geometries and stiffnesses, these methods do not always perform optimally, especially in out-of-distribution settings. We propose, develop, and test the first application of uncertainty quantification to learned surgical soft-tissue manipulation policies as an early identification system for task failures. We analyze two different methods of uncertainty quantification, deep ensembles and Monte Carlo dropout, and find that deep ensembles provide a stronger signal of future task success or failure. We validate our approach using the physical daVinci Research Kit (dVRK) surgical robot to perform physical soft-tissue manipulation. We show that we are able to successfully detect task failure and request human intervention when necessary while still enabling autonomous manipulation when possible. Our learned tissue manipulation policy with uncertainty-based early failure detection achieves a zero-shot sim2real performance improvement of 47.5% over the prior state of the art in learned soft-tissue manipulation. We also show that our method generalizes well to new types of tissue as well as to a bimanual soft-tissue manipulation task. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic surgery

Uncertainty Aware Forward and Inverse Kinematics for Tendon-Driven Continuum Robots via Mixture Density Networks

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Journal of Medical robotics Research 2025年第0期

作者： Thompson, Jordan Cho, Brian Y. Brown, Daniel S. Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake City 84112 UT United States

Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry given the current tendon displacements. This model computes a probability distribution that is more representative of the true distribution of geometries at a given configuration than a model that outputs a single geometry, while also reducing the computation time. We demonstrate uses of this model through both a trajectory optimization method that explicitly reasons about the workspace uncertainty to minimize the probability of collision and an inverse kinematics method that maximizes the likelihood of occupying a desired geometry. © 2025 World Scientific Publishing Company.

关键词： Continuum robots kinematics medical robotics

DefGoalNet: Contextual Goal Learning from Demonstrations for Deformable Object Manipulation

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DefGoalNet: Contextual Goal Learning from Demonstrations for...

IEEE International Conference on robotics and Automation (ICRA)

作者： Bao Thach Tanner Watts Shing-Hei Ho Tucker Hermans Alan Kuntz Robotics Center and Kahlert School of Computing University of Utah Salt Lake City UT USA NVIDIA Corporation Seattle WA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method’s effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical real-world applications.

关键词： Knowledge engineering Heart Shape Neural networks Pipelines Trajectory Task analysis

Efficient and Accurate Mapping of Subsurface Anatomy via Online Trajectory Optimization for Robot Assisted Surgery

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arXiv 2023年

作者： Cho, Brian Y. Kuntz, Alan The Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States

Robotic surgical subtask automation has the potential to reduce the per-patient workload of human surgeons. There are a variety of surgical subtasks that require geometric information of subsurface anatomy, such as the location of tumors, which necessitates accurate and efficient surgical sensing. In this work, we propose an automated sensing method that maps 3D subsurface anatomy to provide such geometric knowledge. We model the anatomy via a Bayesian Hilbert map-based probabilistic 3D occupancy map. Using the 3D occupancy map, we plan sensing paths on the surface of the anatomy via a graph search algorithm, A∗ search, with a cost function that enables the trajectories generated to balance between exploration of unsensed regions and refining the existing probabilistic understanding. We demonstrate the performance of our proposed method by comparing it against 3 different methods in several anatomical environments including a real-life CT scan dataset. The experimental results show that our method efficiently detects relevant subsurface anatomy with shorter trajectories than the comparison methods, and the resulting occupancy map achieves high accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions