Automating a robotic task, e.g., robotic suturing can be very complex and time-consuming. Learning a task model to autonomously perform the task is invaluable making the technology, robotic surgery, accessible for a wider community. The data of robotic surgery can be easily logged where the collected data can be used to learn task models. This will result in reduced time and cost of robotic surgery in which a surgeon can supervise the robot operation or give high-level commands instead of low-level control of the tools. We present a data-set of needle insertion in soft tissue with two arms where Arm 1 inserts the needle into the tissue and Arm 2 actively manipulate the soft tissue to ensure the desired and actual exit points are the same. This is important in real-surgery because suturing without active manipulation of tissue may yield failure of the suturing as the stitch may not grip enough tissue to resist the force applied for the suturing. We present a needle insertion dataset including 60 successful trials recorded by 3 pair of stereo cameras. Moreover, we present Deep-robot Learning from Demonstrations that predicts the desired state of the robot at the time step after t (which the optimal action taken at t yields) by looking at the video of the past time steps, i.e. n step time history where N is the memory time window, of the task execution. The experimental results illustrate our proposed deep model architecture is outperforming the existing methods. Although the solution is not yet ready to be deployed on a real robot, the results indicate the possibility of future development for real robot deployment.
A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Mechanical and Automation Engineering, T Stone Robotics Institute, Shun Hing Institute of Advanced Engineering, Multi-Scale Medical Robotics Center, and Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系、T Stone机器人研究所、Shun Hing先进工程研究所、多尺度医疗机器人中心以及医学智能与XR研究所）
DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations
UC Berkeley（加州大学伯克利分校）
选项是一种短期技能，由状态空间的指定区域的控制策略和识别离开该区域的终止条件组成。在之前的工作中，我们提出了一种名为Deep Discovery of Options（DDO）的算法，用于发现加速Atari游戏中强化学习的选项。本文研究了机器人模仿学习的扩展，称为深度连续选项发现（DDCO），其中通过深度神经网络参数化的低级连续控制技能从演示中学习。我们扩展DDO：（1）混合分类-连续分布模型，用于参数化可以调用离散选项以及连续控制动作的高级策略，以及（2）交叉验证方法，其放宽了DDO的要求，即用户指定要发现的选项的数量。我们评估DDCO在模拟的3连杆机器人在垂直平面上推块摩擦和重力，并在两个物理实验的达芬奇手术机器人，针插入针被抓住，并插入到一个硅胶组织幻影，针仓采摘针和针从一堆抓住并归类到箱子。在3连杆臂模拟中，结果表明，与基线模仿学习方法相比，DDCO可以减少3倍的演示来实现相同的奖励。在针头插入任务中，DDCO成功率为8/10，而下一个最准确的模仿学习基线为6/10。在手术箱拾取任务中，学习的策略在99次尝试抓取中的66次中成功抓取单个物体，并且在除一次之外的所有情况下，通过第二次重试从失败的抓取中成功恢复。
Improving Needle Penetration via Precise Rotational Insertion Using Iterative Learning Control
Dept. of Mechanical and Aerospace Engineering, University of California, Los Angeles（机械与航空航天工程系，加州大学洛杉矶分校）；Jules Stein Eye Institute, University of California, Los Angeles（Jules Stein眼科研究所，加州大学洛杉矶分校）
Safe Uncertainty-Aware Learning of Robotic Suturing
SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark（SDU机器人研究所、马士基麦金尼莫勒研究所、南部丹麦大学）；Department of Mechanical Engineering, Myongji University（机械工程系、明州大学）
Vibration-Based Energy Metric for Restoring Needle Alignment in Autonomous Robotic Ultrasound
Multi-scale Medical Research Center（多尺度医学研究中心）；Department of Translational Surgical Oncology（转化外科肿瘤学系）；Technical University of Munich（慕尼黑技术大学）；The Chinese University of Hong Kong（香港中文大学）；Centre for Tactile Internet with Human-in-the-Loop（人机协同触觉互联网中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；TUD Dresden University of Technology（德累斯顿技术大学）；Federal Ministry of Research, Technology and Space of Germany（德国联邦研究、技术和空间部）；Bavarian Ministry of Science and the Arts（巴伐利亚科学与艺术部）；Saxon Ministry for Science, Culture, and Tourism（萨克森科学、文化和旅游部）
Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks
Autonomous Systems Lab, Technische Universität Wien (TU Wien)（自主系统实验室，维也纳工业大学（TU Wien））；Institute of Robotics and Mechatronics, German Aerospace Center (DLR)（机器人与机电一体化研究所，德国航空航天中心（DLR））
Needle Biopsy And Fiber-Optic Compatible Robotic Insertion Platform
Department of Mechatronics and Robotics（机械电子与机器人系）；Department of Mechanical Science and Engineering（机械科学与工程系）；Beckman Institute for Advanced Science and Technology（先进科学与技术研究所）；Division of Research and Economic Development（研究与经济开发部门）
Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies
Jacobs School of Engineering, University of California San Diego（加州大学圣地亚哥分校 Jacobs 工程学院）；School of Medicine, University of California San Diego（加州大学圣地亚哥分校医学院）；School of Medicine, University of Missouri-Kansas City（密苏里大学堪萨斯城分校医学院）
DAISS: Phase-Aware Imitation Learning for Dual-Arm Robotic Ultrasound-Guided Interventions
Chair for Computer Aided Medical Procedures(CAMP), Technical University of Munich(TUM)（计算机辅助医疗程序主席职位（CAMP），慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The University of Hong Kong(HKU)（香港大学）
MambaXCTrack: Mamba-based Tracker with SSM Cross-correlation and Motion Prompt for Ultrasound Needle Tracking
The Chinese University of Hong Kong（香港中文大学）；State Grid Xin Jiang Electric Power Co., Ltd. Supervoltage Branch（国家电网新疆电力有限公司超高压分公司）；Duke Kunshan University（昆山杜克大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology（脑认知与脑启发智能技术国家重点实验室）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）
Needle Segmentation Using GAN: Restoring Thin Instrument Visibility in Robotic Ultrasound
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右侧伊萨尔医院）；DZHK (German Centre for Cardiovascular Research)（德国心血管研究中心）；Shanghai Tongji Hospital（上海同济医院）；Tongji University School of Medicine（同济大学医学院）；The Chinese University of Hong Kong（香港中文大学）；Multi-scale Medical Robotics Centre（多尺度医学机器人中心）
Automated Robotic Needle Puncture for Percutaneous Dilatational Tracheostomy
Manchester Centre for Robotics and AI（曼彻斯特机器人与人工智能中心）；Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）；University of Manchester（曼彻斯特大学）；Department of Electrical and Electronic Engineering（电子与电气工程系）；Manchester University Foundation Trust Wythenshawe Hospital Intensive Care Unit（曼彻斯特大学基金会韦斯豪斯医院重症监护单元）
Web-based Augmented Reality with Auto-Scaling and Real-Time Head Tracking towards Markerless Neurointerventional Preoperative Planning and Training of Head-mounted Robotic Needle Insertion
The Chinese University of Hong Kong（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学（深圳）研究院）
本文提出一种无标记Web增强现实应用，利用MediaPipe和React Three Fiber实现头部自动缩放与实时跟踪，用于神经介入术前规划和头戴式机器人针插入训练，成本低且支持协作。
University of Utah（犹他大学）；Vanderbilt University（范德堡大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of North Carolina School of Medicine（北卡罗来纳大学医学院）；Vanderbilt University Medical Center（范德堡大学医学中心）
Body-mounted MR-conditional Robot for Minimally Invasive Liver Intervention
Institute of Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center（范德堡大学医学中心范德堡大学影像科学研究所）；Emory University（埃默里大学）；Mayo Clinic（梅奥诊所）；Department of Biomedical Engineering, Georgia Institute of Technology/Emory University（佐治亚理工学院/埃默里大学生物医学工程系）
Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Hospital（约翰斯·霍普金斯医院）；Klinikum rechts der Isar der Technische Universität München（慕尼黑工业大学右岸医院）
An MRI-Guided Robotic System to Improve Hippocampal Access for Epilepsy Interventions
Vanderbilt University（范德堡大学）；Case Western Reserve University（凯斯西储大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；University of Louisville Health（路易斯维尔大学健康中心）
