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A Data-Driven Model with Hysteresis Compensation for I2RIS Robot

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arXiv 2023年

作者： Esfandiari, Mojtaba Zhou, Yanlin Dehghani, Shervin Hadi, Muhammad Munawar, Adnan Phalen, Henry Gehlbach, Peter Taylor, Russell H. Iordachita, Iulian Department of Mechanical Engineering and also Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Technische Universität München München85748 Germany Wilmer Eye Institute Johns Hopkins Hospital BaltimoreMD21287 United States Department of Computer Science The Laboratory for Computational Sensing and Robotics The Johns Hopkins University BaltimoreMD21218 United States

Retinal microsurgery is a high-precision surgery performed on an exceedingly delicate tissue. It now requires extensively trained and highly skilled surgeons. Given the restricted range of instrument motion in the confined intraocular space, and also potentially restricting instrument contact with the sclera, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, space access, and positioning accuracy during retinal procedures requiring high precision and advantageous tooltip approach angles, such as retinal vein cannulation and epiretinal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control, however, as opposed to conventional manipulators, modeling of these robots does not follow a straightforward method due to their complex mechanical structure and actuation mechanisms. Especially, in wire-driven snake-like robots, the hysteresis problem due to the wire tension condition can have a significant impact on the positioning accuracy of these robots. In this paper, we proposed an experimental kinematics model with a hysteresis compensation algorithm using the probabilistic Gaussian mixture models (GMM) Gaussian mixture regression (GMR) approach. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (I2RIS) show that the proposed model provides 0.4◦ accuracy, which is overall 60% and 70% of improvement for yaw and pitch degrees of freedom, respectively, compared to a previous model of this robot. © 2023, CC BY.

关键词： Robots

Representation Learning of Multivariate Time Series using Attention and Adversarial Training

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arXiv 2024年

作者： Scharwächter, Leon Otte, Sebastian Neuro-Cognitive Modeling Group Department of Computer Science University of Tübingen Germany Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany

A critical factor in trustworthy machine learning is to develop robust representations of the training data. Only under this guarantee methods are legitimate to artificially generate data, for example, to counteract imbalanced datasets or provide counterfactual explanations for blackbox decision-making systems. In recent years, Generative Adversarial Networks (GANs) have shown considerable results in forming stable representations and generating realistic data. While many applications focus on generating image data, less effort has been made in generating time series data, especially multivariate signals. In this work, a Transformer-based autoencoder is proposed that is regularized using an adversarial training scheme to generate artificial multivariate time series signals. The representation is evaluated using t-SNE visualizations, Dynamic Time Warping (DTW) and Entropy scores. Our results indicate that the generated signals exhibit higher similarity to an exemplary dataset than using a convolutional network approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

A Data-Driven Model with Hysteresis Compensation for I2RIS Robot

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A Data-Driven Model with Hysteresis Compensation for I2RIS R...

International Symposium on Medical robotics (ISMR)

作者： Mojtaba Esfandiari Yanlin Zhou Shervin Dehghani Muhammad Hadi Adnan Munawar Henry Phalen David E. Usevitch Peter Gehlbach Iulian Iordachita Department of Mechanical Engineering and Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Technische Universität München Munchen Germany Johns Hopkins Hospital Wilmer Eye Institute Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Retinal microsurgery is a high-precision surgery performed on a delicate tissue requiring the skill of highly trained surgeons. Given the restricted range of instrument motion in the confined intraocular space, snake-like robots may prove to be a promising technology to provide surgeons with greater flexibility, dexterity, and positioning accuracy during retinal procedures such as retinal vein cannulation and epireti-nal membrane peeling. Kinematics modeling of these robots is an essential step toward accurate position control. Unlike conventional manipulators, modeling these robots does not fol-low a straightforward method due to their complex mechanical structure and actuation mechanisms. The hysteresis problem can especially impact the positioning accuracy significantly in wire-driven snake-like robots. In this paper, we propose a data-driven kinematics model using a probabilistic Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) approach with a hysteresis compensation algorithm. Experimental results on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (1 2 RIS) show that the proposed model with the hysteresis compensation can predict the snake tip bending angle for pitch and yaw with 0.45° and 0.39° root mean square error (RMSE), respectively. This results in overall 60% and 70% improvements of accuracy for yaw and pitch over the same model without the hysteresis compensation.

关键词： Accuracy Veins Snake robots Kinematics Aerospace electronics Robot sensing systems Retina

Spatial computing and intuitive interaction: Bringing mixed reality and robotics together

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arXiv 2022年

作者： Delmerico, Jeffrey Poranne, Roi Bogo, Federica Oleynikova, Helen Vollenweider, Eric Coros, Stelian Nieto, Juan Pollefeys, Marc Microsoft Mixed Reality And AI Lab Zürich Switzerland Computational Robotics Lab ETH Zürich Switzerland Department Of Computer Science The University Of Haifa Israel NVIDIA United States

Spatial computing-the ability of devices to be aware of their surroundings and to represent this digitally-offers novel capabilities in human-robot interaction. In particular, the combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning, which offers exciting new possibilities for collaboration between humans and robots. This paper presents several human-robot systems that utilize these capabilities to enable novel robot use cases: mission planning for inspection, gesture-based control, and immersive teleoperation. These works demonstrate the power of mixed reality as a tool for human-robot interaction, and the potential of spatial computing and mixed reality to drive the future of human-robot interaction. Copyright © 2022, The Authors. All rights reserved.

关键词： Human robot interaction

Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

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arXiv 2024年

作者： Rahman, Md Abdur Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States iDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13% accuracy, 100% precision, 98.33% recall and 99.15% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks. © 2024, CC BY.

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Estimating the Joint Angles of an Articulated Microrobotic Instrument Using Optical Coherence Tomography

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Estimating the Joint Angles of an Articulated Microrobotic I...

International Symposium on Medical robotics (ISMR)

作者： Erik Fredin Nirmal Pol Anton Zaliznyi Eric Diller Lueder A. Kahrs Department of Mechanical and Industrial Engineering University of Toronto Canada Institute of Biomedical Engineering University of Toronto Canada Institute of Computer Science University of Tartu Estonia Robotics Institute University of Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Canada

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

Pose estimation of surgical tools is necessary for controlling and manipulating the tool in confined surgical regions for minimally invasive surgeries. Several studies have explored the application of robot assisted surgeries which require an imaging system to track the tool and navigate it with high accuracy and precision. Optical Coherence Tomog-raphy (OCT) is an emerging volumetric imaging modality in minimally invasive robot assisted surgery. We present a marker-based computer vision algorithm to estimate the wrist and finger joint angles of a neurosurgical gripper tool from an OCT volume. The tool's joint angles lie on two perpendicular planes. Markers of 1 mm diameter are placed on the tool and then the gripper is imaged under the OCT system. The raw volumetric images are pre-processed by downsampling and applying thresholds. The markers in the OCT volume are then detected using 3D template matching. False positive detections are algorithmically omitted by evaluating the relative distances between the markers. Finally, the positions of these detected markers are used to estimate the joint angles. Our approach yields an average error of 2.20 and a standard deviation of 3.4° for the wrist joint $(\theta_{1})$ . For the finger joint $(\theta_{2})$ , it yields an average error of 2° and a standard deviation of 1.5°• The estimations are provided within 0.5 seconds on a PC with a 16 core CPU.

关键词： Wrist Medical robotics Minimally invasive surgery Three-dimensional displays Optical coherence tomography Fingers Pose estimation

An Initial Step Towards Stable Explanations for Multivariate Time Series Classifiers with LIME

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An Initial Step Towards Stable Explanations for Multivariate...

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Han Meng Christian Wagner Isaac Triguero Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham United Kingdom DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

LIME, or ‘Local Interpretability Model-agnostic Explanations’ is a well-known post-hoc explanation technique for the interpretation of black-box models. While very useful, recent studies show that LIME suffers from stability problems: explanations provided for the same process can be different, making it difficult to trust their reliability. This paper investigates the stability of LIME when explaining multivariate time series classification problems. We demonstrate that due to the temporal dependency in time series data, the traditional artificial neighbour generation methods used in LIME have a higher risk of creating out-of-distribution inputs. We disucss how this behavior is one of the reasons resulting in unstable explanations. In addition, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune, and we show how unsuitable hyperparameters can contribute to the generation of unstable explanations. As a preliminary step towards addressing these issues, we propose to employ a generative approach with an adaptive weighting method in the LIME framework. Specifically, we adopt a generative model based on variational autoencoder to create within-distribution neighbours, reducing the out-of-distribution problem, while the adaptive weight method eliminates the need for user-defined hyperparameters. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework.

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COVIDX: computer-aided diagnosis of COVID-19 and its severity prediction with raw digital chest X-ray scans

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Quantitative Biology 2022年第2期10卷 208-220页

作者： Wajid Arshad Abbasi Syed Ali Abbas Saiqa Andleeb Maryum Bibi Fiaz Majeed Abdul Jaleel Muhammad Naveed Akhtar Computational Biology and Data Analysis Lab Department of Computer Sciences&Information TechnologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Biotechnology Lab Department of ZoologyKing Abdullah CampusUniversity of Azad Jammu&KashmirMuzaffarabadAJ&K 13100Pakistan Department of Software Engineering University of GujratGujrat 50700Pakistan Department of Computer Science (RCET)UETLahore 54000Pakistan Computational and Internet Services Division Pakistan Institute of Engineering and Applied Sciences(PIEAS)Islamabad 44000Pakistan

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the ***: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the ***: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification ***: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this ***: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected ***: We made COVIDX easily accessible through a cloud-based webserver and python code available at https://***/view/wajidarshad/software and https://***/wajidarshad/covidx, respectively.

关键词： coronavirus COVID-19 radiology machine learning chest X-ray contagious infection

Imitation Learning for Robotic Assisted Ultrasound Examination of Deep Venous Thrombosis using Kernelized Movement Primitives

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arXiv 2024年

作者： Dall’Alba, Diego Busellato, Lorenzo Savarimuthu, Thiusius Rajeeth Cheng, Zhuoqi Iturrate, Iñigo The Altair Robotics Lab. Department of Computer Science University of Verona Verona37123 Italy The SDU Robotics The Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense5230 Denmark Ropca ApS Odense5260 Denmark

Vascular diseases are commonly diagnosed using Ultrasound (US) imaging, which can be inconsistent due to its high dependence on the operator’s skill. Among these, Deep Vein Thrombosis (DVT) is a common yet potentially fatal condition, often leading to critical complications like pulmonary embolism. Robotic US Systems (RUSs) aim to improve diagnostic test consistency but face challenges with the complex scanning pattern requiring precise control over US probe pressure, such as the one needed for indirectly detecting occlusions during DVT assessment. This work introduces an imitation learning method based on Kernelized Movement Primitives (KMP) to standardize the contact force profile during US exams by training a robotic controller using sonographer demonstrations. A new recording device design enhances demonstration acquisition, integrating with US probes and enabling seamless force and position data recording. KMPs are used to link scan trajectory and interaction force, enabling generalization beyond the demonstrations. Our approach, evaluated on synthetic models and volunteers, shows that the KMP-based RUS can replicate an expert’s force control and US image quality, even under conditions requiring compression during scanning. It outperforms previous methods using manually defined force profiles, improving exam standardization and reducing reliance on specialized sonographers. © 2024, CC BY.

关键词： Demonstrations