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IEEE International Conference on Soft robotics (RoboSoft)

作者： Thomas George Thuruthel Fumiya Iida Department of Engineering The Bio-Inspired Robotics Lab University of Cambridge UK Department of Computer Science University College London London UK

Soft robots are typically approximated as low-dimensional systems, especially when learning-based methods are used. This leads to models that are limited in their capability to predict the large number of deformation modes and interactions that a soft robot can have. In this work, we present a deep-learning methodology to learn high-dimensional visual models of a soft robot combining multimodal sensorimotor information. The models are learned in an end-to-end fashion, thereby requiring no intermediate sensor processing or grounding of data. The capabilities and advantages of such a modelling approach are shown on a soft anthropomorphic finger with embedded soft sensors. We also show that how such an approach can be extended to develop higher level cognitive functions like identification of the self and the external environment and acquiring object manipulation skills. This work is a step towards the integration of soft robotics and developmental robotics architectures to create the next generation of intelligent soft robots.

关键词： Visualization Shape control Soft sensors Tactile sensors Soft robotics Predictive models Robot sensing systems

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A Review of Autonomous Glaucomatous Grading via OCT Imagery 2

A Review of Autonomous Glaucomatous Grading via OCT Imagery

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Raja, Hina Hassan, Taimur Hassan, Bilal Seneviratne, Lakmal Dias, Jorge Werghi, Naoufel Nust Biomisa Lab Department of Computer and Software Engineering College of Eme Rawalpindi Pakistan Khalifa University C2PS and Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Kucars Department of Mechanical Engineering & Robotics Abu Dhabi United Arab Emirates

ISBN: (纸本)9781665498197

Glaucoma is a progressive and degenerative optic neuropathy that damages the optic nerve due to increased intraocular pressure and yields various visual field abnormalities. Any damage to the optic nerve (due to glaucoma) is irreversible because of the incapacity of the retinal nerve fibers to regenerate. However, the glaucomatous progression can be properly cured if it is diagnosed in the early stages. Furthermore, health authorities worldwide have prioritized the early detection and prevention of glaucomatous progression, and in this regard, presenting a comprehensive insight on the recent literature serves as a critical benchmark to both researchers and clinicians. Towards this end, this paper presents a detailed overview of the state-of-the-art clinical and technical methods related to glaucomatous detection, grading, and prognosis. The clinical work focuses on glaucoma diagnosis and progression tracking in different retinal regions such as the retinal layers, optic never head, and lamina cribrosa, while the technical works focus on producing lesion-aware screening and grading of glaucomatous pathologies via optical coherence tomography. © 2022 IEEE.

关键词： Grading

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Surgical neural radiance fields from one image

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International Journal of computer Assisted Radiology and Surgery 2025年 1-5页

作者： Neri, Alberto Fehrentz, Maximilan Penza, Veronica Mattos, Leonardo S. Haouchine, Nazim Biomedical Robotics Lab Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Computer Science Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genova Italy Technical University of Munich Munich Germany Harvard Medical School Brigham and Women’s Hospital Boston United States

Purpose: Neural radiance fields (NeRF) offer exceptional capabilities for 3D reconstruction and view synthesis, yet their reliance on extensive multi-view data limits their application in surgical intraoperative settings where only limited data are available. This work addresses this challenge by leveraging a single intraoperative image and preoperative data to train NeRF efficiently for surgical scenarios. Methods: We leverage preoperative MRI data to define the set of camera viewpoints and images needed for robust and unobstructed training. Intraoperatively, the appearance of the surgical image is transferred to the pre-constructed training set through neural style transfer, specifically combining WTC² and STROTSS to prevent over-stylization. This process enables the creation of a dataset for instant and fast single-image NeRF training. Results: The method is evaluated with four clinical neurosurgical cases. Quantitative comparisons to NeRF models trained on real surgical microscope images demonstrate strong synthesis agreement, with similarity metrics indicating high reconstruction fidelity and stylistic alignment. When compared with ground truth, our method demonstrates high structural similarity, confirming good reconstruction quality and texture preservation. Conclusion: Our approach demonstrates the feasibility of single-image NeRF training in surgical settings, overcoming the limitations of traditional multi-view methods. By eliminating the dependency on a large multi-view dataset, our method offers a faster, more adaptable solution for generating accurate 3D reconstructions in real-time surgical scenarios. © CARS 2025.

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Exploring Help-Seeking Behavior, Performance, and Cognitive Load in Individual Tutoring: A Comparative Study between Human Tutors and Social Robots

Exploring Help-Seeking Behavior, Performance, and Cognitive ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xiaoxuan Hei Heng Zhang Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Social robots have become increasingly prevalent in the context of one-on-one tutoring, serving as effective educational aids. In response to this trend, the present study was devised to conduct a comparative analysis between human tutors and robot tutors. Additionally, the study aims to investigate how varying previous knowledge in robots influence students’ tendencies for help-seeking. By examining the performance and physiological signals of participants, this research seeks to provide valuable insights into the effectiveness of social robots in educational contexts. 21 participants were divided into three groups, each seeking assistance from a human tutor (HT), seeking help from a robot without any prior knowledge of robots (RT1), and seeking help from a robot after gaining some understanding of its capabilities (RT2). Our results demonstrated that participants sought more help from robot than from human and participants in Group RT2 performed better than participants in Group RT1. However, participants experienced greater cognitive load when interacting with a robot tutor compared to interacting with a human tutor. Future work could focus on developing interventions to alleviate students’ cognitive load during interactions with robot tutors.

关键词： Social robots Robot vision systems Cognitive load Market research Cameras Physiology Behavioral sciences Robots Thermal stresses Stress

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Fast Spatial Reasoning of Implicit 3D Maps through Explicit Near-Far Sampling Range Prediction

Fast Spatial Reasoning of Implicit 3D Maps through Explicit ...

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

作者： Chaerin Min Sehyun Cha Changhee Won Jongwoo Lim Department of Computer Science Brown University Advanced Robotics Lab. CTO Division LG Electronics MultiplEYE Co. Ltd Department of Mechanical Engineering Seoul National University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D mapping is critical for many robotics applications, such as autonomous navigation and object manipulation. Recently, deep implicit mapping approaches have received much attention for their compactness and ability to represent fine-grained details. However, without explicit guidance, such implicit representations are often cumbersome for searching the full range on the rays to find the object surfaces. As a result, several approaches, including hierarchical sampling, occupancy grids, and zero-level set baking, have been proposed to improve sampling where costly forward passes of the neural network should be performed. However, hierarchical sampling is still suboptimal in that it requires uniform coarse samples. Discrete occupancy grids of Instant NGP and zero-level sets of various baking methods are less suitable for large and noisy real *** this paper, we present a novel framework for adaptively predicting the near-far range for sampling the query positions of the deep implicit map. For this purpose, the truncated signed distance grid for the map is pre-constructed and used to provide hints for near-far prediction during rendering. In addition, our recovery algorithm automatically detects failed near-far predictions and recovers only those rays by directly using the implicit map. We conduct extensive experiments on a synthetic dataset, a public real dataset, and a real dataset captured by our multi-camera robot system. The experimental results show that our algorithm achieves the same rendering quality with surprisingly fewer samples compared to the existing methods, which means that the robot can reason about the image and depth properties of the scene much faster. Finally, a thorough analysis of the sample distribution along the rays is provided to give a better understanding of our method’s strong efficiency, adaptability, and robustness. https://***/TSDF-sampling/

关键词： Three-dimensional displays Neural networks Rendering (computer graphics) Prediction algorithms Search problems Robustness Cognition Noise measurement Intelligent robots Synthetic data

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FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

arXiv

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Luo, Wentao Liu, Boyi Zhang, Ziyang Wang, Yaoyuan Liu, Jingtai Institute of Robotics and Automatic Information System Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China Advanced Computing and Storage Lab Huawei 2012 Lab China Department of Electronic and Computer Engineering The Kong Kong University of Science and Technology Hong Kong

Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue within a reinforcement learning-based framework—is introduced. A structured method for identifying guidance points is developed, consisting of obstacle boundary extraction, potential guidance point detection, and redundancy elimination. To integrate guidance points into the navigation pipeline, a perception-to-planning mapping strategy is proposed, unifying guidance points with other perceptual inputs and enabling the RL agent to effectively leverage the complementary relationships among raw laser data, human detection and tracking, and guidance points. Qualitative and quantitative simulations demonstrate that the proposed approach achieves the highest success rate and near-optimal travel times, greatly improving both safety and efficiency. Furthermore, real-world experiments in dynamic corridors and lobbies validate the robot’s ability to confidently navigate around obstacles and robustly avoid pedestrians. © 2025, CC BY.

关键词： Travel time

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ASFM: Augmented Social Force Model for Legged Robot Social Navigation

ASFM: Augmented Social Force Model for Legged Robot Social N...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sebastian Ægidius Rodrigo Chacón-Quesada Andromachi Maria Delfaki Dimitrios Kanoulas Yiannis Demiris Department of Computer Science Robot Perception and Learning Lab University College London Gower Street London UK Department of Electrical and Electronic Engineering Personal Robotics Lab Imperial College London London UK Department of Computer Science AI Centre UCL and the Archimedes/Athena RC Greece

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Social navigation in robotics primarily involves guiding mobile robots through human-populated areas, with pedestrian comfort balanced with efficient path-finding. Although progress has been seen in this field, a solution for the seamless integration of robots into pedestrian settings remains elusive. In this paper, a social force model for legged robots is developed, utilizing visual perception for human localization. In particular, an augmented social force model is introduced, incorporating refined interpretations of repulsive forces and avoidance behaviors based on pedestrian actions, alongside a target following mechanism. Experimental evaluation on a quadruped robot, through various scenarios, including interactions with oncoming pedestrians, crowds, and obstructed paths, demonstrates that the proposed augmented model significantly improves upon previous baseline methods in terms of chosen path length, average velocity, and time-to-goal for effective and efficient social navigation. The code isopen-source, while video demonstrations can be found on the project’s webpage: https://***/ASFM/

关键词： Legged locomotion Location awareness Pedestrians Codes Navigation Force Humanoid robots Quadrupedal robots Visual perception

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A Stable Guidance Method for Teleoperation-based Robot Learning from Demonstration

A Stable Guidance Method for Teleoperation-based Robot Learn...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Weiyong Si Zhehao Jin Zhenyu Lu Ning Wang Chenguang Yang School of Computer Science and Electronic Engineering at the University of Essex Colchester UK College of Information Engineering Zhejiang University of Technology Hangzhou China Bristol Robotics Lab at the University of the West of England Bristol UK Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robot learning from teleoperation-based demonstration has been proven to be an effective method for robot skills learning. However, the human demonstration process is mentally and physically challenging due to the dexterous and continuous interaction control. Especially, for contact-rich tasks human operators need to control the pose and contact force between the end-effector and the environment, only based on visual and haptic feedback. To tackle the problem, this work studies a stable guidance method for teleoperation-based demonstration to improve the experience and performance of human demonstration and reduce the workload of human operators. The guidance design is based on a dynamic system-based imitation learning model, which is achieved by human demonstration. The virtual guidance force will decrease as the virtual guidance’s iterative learning. The efficiency of human demonstration is improved, and human operators’ physical and mental workload is reduced. Two typical experiments, robot-assisted polishing and robot-assisted ultrasound scanning, are conducted to validate the performance of the guidance method.

关键词： Visualization Ultrasonic imaging Imitation learning Force Dynamics Process control Robot learning End effectors Haptic interfaces Iterative methods

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

iMTSP: Solving Min-Max Multiple Traveling Salesman Problem w...

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

作者： Yifan Guo Zhongqiang Ren Chen Wang Flight Dynamics & Control/Hybrid Systems Lab Purdue University West Lafayette IN Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Computer Science and Engineering Spatial AI & Robotics (SAIR) Lab University at Buffalo NY USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents).

关键词： Urban areas Estimation Reinforcement learning Traveling salesman problems Internet Resource management Optimization Intelligent robots Faces Convergence

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