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Safer Motion Planning of Steerable Needles via a Shaft-to-Tissue Force Model

Journal of Medical robotics Research 2023年第1-2期8卷

作者： Bentley, Michael Rucker, Caleb Reddy, Chakravarthy Salzman, Oren Kuntz, Alan Robotics Center and Kahlert School of Computing University of Utah Salt Lake City 84112 UT United States The Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee Knoxville 37996 TN United States Huntsman Cancer Institute and School of Medicine University of Utah Salt Lake City 84112 UT United States Department of Computer Science Technion-Israel Institute of Technology Technion City Haifa 3200003 Israel

Steerable needles are capable of accurately targeting difficult-to-reach clinical sites in the body. By bending around sensitive anatomical structures, steerable needles have the potential to reduce the invasiveness of many medical procedures. However, inserting these needles with curved trajectories increases the risk of tissue damage due to perpendicular forces exerted on the surrounding tissue by the needle's shaft, potentially resulting in lateral shearing through tissue. Such forces can cause significant tissue damage, negatively affecting patient outcomes. In this work, we derive a tissue and needle force model based on a Cosserat string formulation, which describes the normal forces and frictional forces along the shaft as a function of the planned needle path, friction model and parameters, and tip piercing force. We propose this new force model and associated cost function as a safer and more clinically relevant metric than those currently used in motion planning for steerable needles. We fit and validate our model through physical needle robot experiments in a gel phantom. We use this force model to define a bottleneck cost function for motion planning and evaluate it against the commonly used path-length cost function in hundreds of randomly generated three-dimensional (3D) environments. Plans generated with our force-based cost show a 62% reduction in the peak modeled tissue force with only a 0.07% increase in length on average compared to using the path-length cost in planning. Additionally, we demonstrate planning with our force-based cost function in a lung tumor biopsy scenario from a segmented computed tomography (CT) scan. By directly minimizing the modeled needle-to-tissue force, our method may reduce patient risk and improve medical outcomes from steerable needle interventions. © 2023 World Scientific Publishing Company.

关键词： bottleneck cost function continuum robots Force modeling motion planning robotic needle steering

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Riemannian Optimistic Algorithms

arXiv

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arXiv 2023年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2006 Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Automation Nanjing University of Science and Technology Jiangsu210023 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China College of Computing Georgia Institute of Technology AtlantaGA30339 United States College of Control Science and Engineering Zhejiang University Zhejiang310058 China

In this paper, we consider Riemannian online convex optimization with dynamic regret. First, we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient Descent (R-OOGD) and the Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD), which combine the advantages of classical optimistic algorithms with the rich geometric properties of Riemannian manifolds. We analyze the dynamic regrets of the R-OOGD and R-AOOGD in terms of regularity of the sequence of cost functions and comparators. Next, we apply the R-OOGD to Riemannian zero-sum games, leading to the Riemannian Optimistic Gradient Descent Ascent algorithm (R-OGDA). We analyze the average iterate and best-iterate of the R-OGDA in seeking Nash equilibrium for a two-player, zero-sum, g-convex-concave games. We also prove the last-iterate convergence of the R-OGDA for g-strongly convex-strongly concave problems. Our theoretical analysis shows that all proposed algorithms achieve results in regret and convergence that match their counterparts in Euclidean spaces. Finally, we conduct several experiments to verify our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Development of a novel computational model for evaluating fall risk in patient room design

arXiv

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arXiv 2020年

作者： Novin, Roya Sabbagh Taylor, Ellen Hermans, Tucker Merryweather, Andrew Department of Mechanical Engineering and Robotics Center University of Utah United States Center for Health Design ConcordCA United States School of Computing and Robotics Center University of Utah United States

Objectives: This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall. Background: While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk. Methods: We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction. Results: Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors, and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features. Conclusions: This innovative approach to room design evaluation and resulting estimation of patient fall risk show promise as proactive evidence-based tool to evaluate the relationship of potential fall risk and room design. The development of the model highlights the challenge of heterogeneity in factors and reporting found in the studies of patient falls, which hinder our understanding of the role of the built environment in mitigating risk. A more comprehensive investigation comparing the model with actual patient falls data is needed to further refine model development. Copyright © 2020, The Authors. All rights reserved.

关键词： Risk assessment

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From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

arXiv

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arXiv 2025年

作者： Acar, Ayberk Smith, Mariana Al-Zogbi, Lidia Watts, Tanner Li, Fangjie Li, Hao Yilmaz, Nural Scheikl, Paul Maria d’Almeida, Jesse F. Sharma, Susheela Branscombe, Lauren Ertop, Tayfun Efe Webster, Robert J. Oguz, Ipek Kuntz, Alan Krieger, Axel Wu, Jie Ying Department of Computer Science Vanderbilt University NashvilleTN37235 United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD21211 United States Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37235 United States Virtuoso Surgical NashvilleTN37205 United States Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KnoxvilleTN37996 United States

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy, however this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally invasive surgeries in tight spaces but additional processing is required to generate 3D scene understanding. We propose a 3D mapping pipeline that uses only RGB images to create segmented point clouds of the target anatomy. To ensure the most precise reconstruction, we compare different structure from motion algorithms’ performance on mapping the central airway obstructions, and test the pipeline on a downstream task of tumor resection. In several metrics, including post-procedure tissue model evaluation, our pipeline performs comparably to RGB-D cameras and, in some cases, even surpasses their performance. These promising results demonstrate that automation guidance can be achieved in minimally invasive procedures with monocular cameras. This study is a step toward the complete autonomy of surgical robots. © 2025, CC BY-NC-ND.

关键词： Image segmentation

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Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation

arXiv

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arXiv 2021年

作者： Zhang, Tiantian Wang, Xueqian Liang, Bin Yuan, Bo The Intelligent Computing Lab Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Research Center for Navigation and Control Department of Automation Tsinghua University Beijing100084 China

The powerful learning ability of deep neural networks enables reinforcement learning agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume i.i.d. inputs, which unfortunately does no hold in the general reinforcement learning paradigm where the training data is temporally correlated and non-stationary. This issue may lead to the phenomenon of "catastrophic interference" and the collapse in performance. In this paper, we present IQ, i.e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multi-head network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks, IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at: https://***/Sweety-dm/Interference-aware-Deep-Q-learning. © 2021, CC BY-NC-SA.

关键词： Reinforcement learning

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In-hand object-dynamics inference using tactile fingertips

arXiv

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arXiv 2020年

作者： Sundaralingam, Balakumar Hermans, Tucker University of Utah Robotics Center School of Computing University of Utah Salt Lake CityUT United States

Having the ability to estimate an object’s properties through interaction will enable robots to manipulate novel objects. Object’s dynamics, specifically the friction and inertial parameters have only been estimated in a lab environment with precise and often external sensing. Could we infer an object’s dynamics in the wild with only the robot’s sensors? In this paper, we explore the estimation of dynamics of a grasped object in motion, with tactile force sensing at multiple fingertips. Our estimation approach does not rely on torque sensing to estimate the dynamics. To estimate friction, we develop a control scheme to actively interact with the object until slip is detected. To robustly perform the inertial estimation, we setup a factor graph that fuses all our sensor measurements on physically consistent manifolds and perform inference. We show that tactile fingertips enable in-hand dynamics estimation of low mass objects. Copyright © 2020, The Authors. All rights reserved.

关键词： Friction

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Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 9737-9757页

作者： Yuji Cao Huan Zhao Yuheng Cheng Ting Shu Yue Chen Guolong Liu Gaoqi Liang Junhua Zhao Jinyue Yan Yun Li Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Center for Crowd Intelligence Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Jurong West Singapore School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China i4AI Ltd. London U.K.

With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as multitask learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared with conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs’ functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated and provide insights into future directions. Finally, the comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications, such as robotics, autonomous driving, and energy systems.

关键词： Taxonomy Surveys Visualization Reviews Planning Reinforcement learning Games Transformers Natural language processing Large language models

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Bipartite Tracking Consensus for General Linear Multi-Agent Systems with Asynchronous Communications over Signed Networks

Bipartite Tracking Consensus for General Linear Multi-Agent ...

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第40届中国控制会议

作者： Runyao Chen Lulu Chen Jinliang Shao Wei Xing Zheng School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China School of Automation Engineering University of Electronic Science and Technology of China Research Center on Crowd Spectrum Intelligence Shenzhen Institute of Artifcial Intelligence and Robotics for Society School of Computing Engineering and Mathematics Western Sydney University

In this paper, the bipartite tracking consensus problem of general linear multi-agent systems over signed networks is investigated, in which an asynchronous communication manner is adopted. The asynchronous setting refers to that the clocks of each agent, at which the agent’s dynamics is updated, are independent of others. Relying on some related properties of the sub-stochastic matrix, the dynamics of the agents under the proposed asynchronous consensus protocol is rigorously *** is shown that the bipartite tracking consensus is implemented if the signed digraph contains a spanning tree. A simulation example is provided to verify our theoretical findings.

关键词： Bipartite tracking consensus Asynchronous communications General linear multi-agent systems Signed networks

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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Multi-Fingered Active Grasp Learning

Multi-Fingered Active Grasp Learning

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Qingkai Lu Mark Van der Merwe Tucker Hermans School of Computing and the Robotics Center University of Utah Salt Lake City UT USA NVIDIA Seattle WA USA

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Learning-based approaches to grasp planning are preferred over analytical methods due to their ability to better generalize to new, partially observed objects. However, data collection remains one of the biggest bottlenecks for grasp learning methods, particularly for multi-fingered hands. The relatively high dimensional configuration space of the hands coupled with the diversity of objects common in daily life requires a significant number of samples to produce robust and confident grasp success classifiers. In this paper, we present the first active deep learning approach to grasping that searches over the grasp configuration space and classifier confidence in a unified manner. We base our approach on recent success in planning multi-fingered grasps as probabilistic inference with a learned neural network likelihood function. We embed this within a multi-armed bandit formulation of sample selection. We show that our active grasp learning approach uses fewer training samples to produce grasp success rates comparable with the passive supervised learning method trained with grasping data generated by an analytical planner. We additionally show that grasps generated by the active learner have greater qualitative and quantitative diversity in shape.

关键词： Training Shape Supervised learning Neural networks Grasping Probabilistic logic Planning

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