IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation最新文献

{"title":"GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation.","authors":"Hochul Hwang, Soowan Yang, Anh N H Nguyen, Parth Goel, Krisha Adhikari, Sunghoon I Lee, Joydeep Biswas, Nicholas A Giudice, Donghyun Kim","doi":"10.1109/icra57385.2026.11697415","DOIUrl":"https://doi.org/10.1109/icra57385.2026.11697415","url":null,"abstract":"<p><p>Tactile Walking Surface Indicators (TWSIs) are safety-critical landmarks that blind and low-vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI segmentation for navigation assistance of BLV individuals. Achieving such reliability requires large-scale annotated data. However, TWSIs are severely underrepresented in existing urban perception datasets, and even existing dedicated paving datasets are limited: they lack robot-relevant viewpoints (e.g., egocentric or top-down) and are geographically biased toward East Asian <i>directional bars</i>- raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks <i>truncated domes</i>- rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar-centric data struggle to generalize to dome-based warnings, leading to missed detections and false stops in safety-critical environments. We introduce GuideTWSI, the largest and most diverse TWSI dataset, which combines a photorealistic synthetic dataset, carefully curated open-source tactile data, and quadruped real-world data collected and annotated by the authors. Notably, we developed an Unreal Engine-based synthetic data generation pipeline to obtain segmented, labeled data across diverse materials, lighting conditions, weather, and robotrelevant viewpoints. Extensive evaluations show that synthetic augmentation improves truncated dome segmentation across diverse state-of-the-art models, with gains of up to +29 mIoU points, and enhances cross-domain robustness. Moreover, real-robot experiments demonstrate accurate stoppings at truncated domes, with high repeatability and stop success rates (96.15%). The GuideTWSI dataset, model weights, and code are publicly released in https://guidedogrobot-tactile.github.io/.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2026 ","pages":"15788-15795"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots.","authors":"Navid Feizi, Filipe C Pedrosa, Rajni V Patel, Jagadeesan Jayender","doi":"10.1109/icra57385.2026.11695877","DOIUrl":"10.1109/icra57385.2026.11695877","url":null,"abstract":"<p><p>Modeling concentric tube robots (CTRs) involves complex nonlinear continuum mechanics, and despite recent progress, physics-based models often lack an accurate representation of the experimental setups. To overcome these limitations, deep neural network-based models have been explored as alternatives with superior accuracy; however, they often overlook known mechanics, require large training datasets, and typically discard shape estimation of the robot. We present a physics-informed neural network (PINN) for kinematic modeling of a 6-DoF CTR with three pre-curved tubes that embed the Cosserat rod differential equations and learns from few-shot observational data, balancing physics priors with data-driven fitting. PINN enables full-state estimation of shape, twist angle, torsional strain, bending moment, and orientation. Benchmark tests show a mean shape error below 1% of the robot length and accurately recovers other kinematic states, outperforming a purely physics-based Cosserat rod model baseline while using a minimal training set. The resulting model is also computationally efficient and robust, making it well-suited for real-time control applications.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2026 ","pages":"10107-10114"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motion Compensation and Adaptive Force Control via iOCT-FBG Sensor Fusion for Robotic Subretinal Injection.","authors":"Aoqi Long, Tianle Wu, Chongyang She, Mojtaba Esfandiari, Peter Gehlbach, Russell H Taylor, Iulian Iordachita","doi":"10.1109/icra57385.2026.11695736","DOIUrl":"https://doi.org/10.1109/icra57385.2026.11695736","url":null,"abstract":"<p><p>Subretinal injection is a highly delicate procedure that demands micron-level precision to avoid irreversible retinal damage. Current robotic systems achieve accurate positioning but remain limited by retinal motion and the lack of tip-force feedback. We present the first adaptive tip-force compensation framework for robotic subretinal injection, fusing intraoperative optical coherence tomography (iOCT) vision with fiber Bragg grating (FBG) force sensing. Our architecture integrates a finite-state machine (FSM) for surgical phase coordination, a Long Short-Term Memory (LSTM) enhanced residual Kalman filter for real-time motion prediction, and an adaptive compliance estimator for safe force regulation. Compared to previous vision-only and force-only method, <i>ex vivo</i> experiments on porcine eyes demonstrate robust improvements: the root-mean-square tracking error reduced by 40% (to <math><mn>18</mn> <mo>.</mo> <mn>5</mn> <mspace></mspace> <mi>μ</mi> <mi>m</mi></math> ), the maximum absolute error lowered by 2.5 times, and 96.7% of tip forces maintained within ±0.7 mN. Control delays were minimized to 0.25 s, enabling low-latency corrections beyond freehand capabilities. Our system enhances precision and safety in fragile retinal tissues, advancing the potential for reliable robot-assisted surgeries for retinal diseases.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2026 ","pages":"13089-13096"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13664642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control.","authors":"Shangqun Yu, Hochul Hwang, Trung M Dang, Joydeep Biswas, Nicholas A Giudice, Sunghoon Ivan Lee, Donghyun Kim","doi":"10.1109/icra57385.2026.11696250","DOIUrl":"https://doi.org/10.1109/icra57385.2026.11696250","url":null,"abstract":"<p><p>A quadruped robot is a promising system that can offer assistance comparable to that of guide dogs due to its similar form factor. However, various challenges remain in making these robots a reliable option for blind and low-vision (BLV) individuals. Among these challenges, noise and jerky motion during walking are critical drawbacks of existing quadruped robots. While these issues have largely been overlooked in guide dog robot research, our interviews with guide dog handlers and trainers revealed that acoustic and physical disturbances can be particularly disruptive for BLV individuals, who rely heavily on environmental sounds for navigation. To address these issues, we developed a novel walking controller for slow stepping and smooth foot swing/contact while maintaining human walking speed, as well as robust and stable balance control. The controller integrates with a perception system to facilitate locomotion over non-flat terrains, such as stairs. Our controller was extensively tested on the Unitree Go1 robot and, when compared with other control methods, demonstrated significant noise reduction - half of the default locomotion controller. To evaluate the usability, workload, and perceived noise of the developed system from a user's perspective, we conducted indoor walking experiments. In these tests, participants compared our controller with the robot's default controller. The results demonstrated higher user acceptance of our controller, highlighting its potential to improve the overall user experience of robotic guide dogs. Video demonstration (best viewed with audio) available at: https://guidedogrobot-stairclimbing.github.io.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2026 ","pages":"23766-23773"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13663884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To Ask or Not To Ask: Human-in-the-loop Contextual Bandits with Applications in Robot-Assisted Feeding.","authors":"Rohan Banerjee, Rajat Kumar Jenamani, Sidharth Vasudev, Amal Nanavati, Katherine Dimitropoulou, Sarah Dean, Tapomayukh Bhattacharjee","doi":"10.1109/icra55743.2025.11127795","DOIUrl":"10.1109/icra55743.2025.11127795","url":null,"abstract":"<p><p>Robot-assisted bite acquisition involves picking up food items with varying shapes, compliance, sizes, and textures. Fully autonomous strategies may not generalize efficiently across this diversity. We propose leveraging feedback from the care recipient when encountering novel food items. However, frequent queries impose a workload on the user. We formulate human-in-the-loop bite acquisition within a contextual bandit framework and introduce LINUCB-QG, a method that selectively asks for help using a predictive model of querying workload based on query types and timings. This model is trained on data collected in an online study involving 14 participants with mobility limitations, 3 occupational therapists simulating physical limitations, and 89 participants without limitations. We demonstrate that our method better balances task performance and querying workload compared to autonomous and always-querying baselines and adjusts its querying behavior to account for higher workload in users with mobility limitations. We validate this through experiments in a simulated food dataset and a user study with 19 participants, including one with severe mobility limitations. Please check out our project website at: emprise.cs.cornell.edu/hilbiteacquisition/.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2025 ","pages":"1378-1384"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12810515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time Deformation-aware Control for Autonomous Robotic Subretinal Injection under iOCT Guidance.","authors":"Demir Arikan, Peiyao Zhang, Michael Sommersperger, Shervin Dehghani, Mojtaba Esfandiari, Russel H Taylor, M Ali Nasseri, Peter Gehlbach, Nassir Navab, Iulian Iordachita","doi":"10.1109/icra55743.2025.11128595","DOIUrl":"10.1109/icra55743.2025.11128595","url":null,"abstract":"<p><p>Robotic platforms provide consistent and precise tool positioning that significantly enhances retinal microsurgery. Integrating such systems with intraoperative optical coherence tomography (iOCT) enables image-guided robotic interventions, allowing autonomous performance of advanced treatments, such as injecting therapeutic agents into the subretinal space. However, tissue deformations due to tool-tissue interactions constitute a significant challenge in autonomous iOCT-guided robotic subretinal injections. Such interactions impact correct needle positioning and procedure outcomes. This paper presents a novel method for autonomous subretinal injection under iOCT guidance that considers tissue deformations during the insertion procedure. The technique is achieved through real-time segmentation and 3D reconstruction of the surgical scene from densely sampled iOCT B-scans, which we refer to as B⁵-scans. Using B⁵-scans we monitor the position of the instrument relative to a virtual target layer between the ILM and RPE. Our experiments on <i>ex-vivo</i> porcine eyes demonstrate dynamic adjustment of the insertion depth and overall improved accuracy in needle positioning compared to prior autonomous insertion approaches. Compared to a 35% success rate in subretinal bleb generation with previous approaches, our method reliably created subretinal blebs in 90% our experiments. The source code and data used in this study are publicly available on GitHub.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2025 ","pages":"10531-10537"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A System for Endoscopic Submucosal Dissection Featuring Concentric Push-Pull Manipulators.","authors":"Peter Connor, Carter Hatch, Khoa Dang, Tony Qin, Ron Alterovitz, Caleb Rucker, Robert J Webster","doi":"10.1109/icra55743.2025.11127293","DOIUrl":"10.1109/icra55743.2025.11127293","url":null,"abstract":"<p><p>Endoscopic Submucosal Dissection (ESD) is an effective minimally invasive approach to removing colon cancer, yet it is underutilized, since it is challenging to learn and perform. To promote the adoption of ESD by making it easier, we propose a system in which two small, flexible robotic manipulators are delivered through a colonoscope. Our system differs from prior robotic systems aimed at this application in that our manipulators are small enough to fit through a clinically used colonoscope. By not re-engineering the colonoscope, we maintain overall system diameter at the current clinical gold standard, and streamline the path to eventual clinical deployment. Our concentric push-pull robot (CPPR) manipulators offer dexterity and simultaneously provide a conduit for grasper or cutting tool deployment. Each manipulator in our system consists of two push-pull tube pairs, and we describe how they are actuated. We describe for the first time our approach to compensating for undesirable CPPR tip motion induced by differences in the tubes' transmission stiffness. We also evaluate the workspace of the manipulators and demonstrate teleoperation in a point-touching experiment. Lastly, we demonstrate the ability of the system to resect tissue via ex vivo animal experiments.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2025 ","pages":"3496-3501"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13541235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully Distributed Shape Sensing of a Flexible Surgical Needle Using Optical Frequency Domain Reflectometry for Prostate Interventions.","authors":"Jacynthe Francoeur, Dimitri Lezcano, Yernar Zhetpissov, Raman Kashyap, Iulian Iordachita, Samuel Kadoury","doi":"10.1109/icra57147.2024.10610256","DOIUrl":"10.1109/icra57147.2024.10610256","url":null,"abstract":"<p><p>In minimally invasive procedures such as biopsies and prostate cancer brachytherapy, accurate needle placement remains challenging due to limitations in current tracking methods related to interference, reliability, resolution or image contrast. This often leads to frequent needle adjustments and reinsertions. To address these shortcomings, we introduce an optimized needle shape-sensing method using a fully distributed grating-based sensor. The proposed method uses simple trigonometric and geometric modeling of the fiber using optical frequency domain reflectometry (OFDR), without requiring prior knowledge of tissue properties or needle deflection shape and amplitude. Our optimization process includes a reproducible calibration process and a novel tip curvature compensation method. We validate our approach through experiments in artificial isotropic and inhomogeneous animal tissues, establishing ground truth using 3D stereo vision and cone beam computed tomography (CBCT) acquisitions, respectively. Our results yield an average RMSE ranging from 0.58 ± 0.21 mm to 0.66 ± 0.20 mm depending on the chosen spatial resolution, achieving the submillimeter accuracy required for interventional procedures.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2024 ","pages":"17594-17601"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11507468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a Unified Approach for Continuously-Variable Impedance Control of Powered Prosthetic Legs over Walking Speeds and Inclines.","authors":"Albert J Lee, Curt A Laubscher, T Kevin Best, Robert D Gregg","doi":"10.1109/icra57147.2024.10610071","DOIUrl":"10.1109/icra57147.2024.10610071","url":null,"abstract":"<p><p>Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride. In contrast, this paper presents a controller that uses smooth impedance parameter trajectories throughout the gait, unifying the stance and swing periods under a single, continuous model. Furthermore, this paper proposes a basis model to represent intertask relationships in the impedance parameters-a strategy that has previously been shown to improve model accuracy over classic linear interpolation methods. In the proposed controller, a weighted sum of Fourier series is used to model the impedance parameters of each joint as continuous functions of gait cycle progression and task. Fourier series coefficients are determined via convex optimization such that the controller best reproduces the joint torques and kinematics in a reference able-bodied dataset. Experiments with a powered knee-ankle prosthesis show that this simpler, unified model produces competitive results when compared to a more complex hybrid impedance-kinematic model over varying walking speeds and inclines.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2024 ","pages":"944-950"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11426229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery.","authors":"Simon Pannek, Shervin Dehghani, Michael Sommersperger, Peiyao Zhang, Peter Gehlbach, M Ali Nasseri, Iulian Iordachita, Nassir Navab","doi":"10.1109/icra57147.2024.10610807","DOIUrl":"https://doi.org/10.1109/icra57147.2024.10610807","url":null,"abstract":"<p><p>Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch's membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical interventions. This has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force data. We introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset's applicability.</p>","PeriodicalId":73286,"journal":{"name":"IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation","volume":"2024 ","pages":"16999-17005"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11501085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}