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IEEE International Conference on Mechatronics and Automation

作者： Zhihui Xu Zongze Li Shoubin Liu Chengzhi Hu Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Mechanical and Electrical Engineering Harbin Institute of Technology Shenzhen China

Capsule robots have emerged as a promising tool for the early diagnosis of gastrointestinal diseases. Unlike conventional gastroscopy, they offer the potential for minimally invasive or even non-invasive treatment, and can alleviate discomfort and pain in patients. However, current capsule robots have some limitations. For instance, they lack stomach tissue biopsy and drug administration functions, and the capsule robot used for biopsy does not consider the tolerance of gastrointestinal tissue. Similarly, the capsule robot used for drug administration does not account for the outlet speed of drug administration. This paper proposes a new biopsy method using a magnetically manipulated capsule robot and analyzes the principles governing the robot's manipulation during the biopsy and drug administration processes. Furthermore, an ANSYS gastric model was established and used to simulate and experimentally analyze damage to the gastric tissue model from different types of biopsy needles and their moving speeds. The appropriate type of biopsy needle and moving speed were determined to prevent damage to the gastric tissue. The study also used FLUENT to determine the appropriate thrust at the entrance of the drug cavity and the impact of the drug density on the drug outlet velocity to ensure complete adherence of the drug to the lesion.

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. Human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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Quantum Error Correction in Green Communications: Towards Sustainable Quantum Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Thakur, Vikram Singh Kumar, Atul Dev, Kapal Department of Electronics Engineering Varanasi221005 India Munster Technological University Department of Computer Science CorkT12 P928 Ireland Department of Institute of Intelligent Systems University of Johannesburg Auckland Park2006 South Africa Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

This work explores the integration of Quantum Error Correction (QEC) techniques within energy-efficient green communication networks, emphasizing their pivotal role in achieving sustainability in next-generation Information and Communication Technology (ICT) systems. Quantum technologies, particularly error correction protocols like surface codes are indispensable for reliable quantum information processing. However, their implementation often incurs substantial energy overhead due to the extensive physical qubit resources required. This research proposes a framework for adapting scalable QEC algorithms, leveraging innovations such as reconfigurable atom arrays, to optimize energy consumption while preserving high fidelity in quantum operations. Surface codes, a cornerstone of QEC, encode logical qubits in two-dimensional lattices of physical qubits, utilizing stabilizer measurements to detect and correct bit-flip and phase-flip errors. The performance of surface codes is influenced by critical parameters such as code distance, which determines fault tolerance, and gate fidelity, which impacts logical error rates. Through a comprehensive analysis of these parameters, this work evaluates the interplay between code distance, qubit array size, and gate fidelity to highlight their collective impact on fault tolerance and computational accuracy. These insights align with global efforts to reduce the environmental footprint of emerging quantum technologies, advancing the principles of green ICT and environmentally conscious innovation. © 2025 IEEE.

关键词： Qubits

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Improving Sample Efficiency of Example-Guided Deep Reinforcement Learning for Bipedal Walking

Improving Sample Efficiency of Example-Guided Deep Reinforce...

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IEEE-RAS International Conference on Humanoid Robots

作者： Rustam Galljamov Guoping Zhao Boris Belousov André Seyfarth Jan Peters Department of Computer Science Intelligent Autonomous Systems Lab TU Darmstadt Germany Lauflabor Locomotion Laboratory Institute of Sport Science TU Darmstadt Germany

ISBN: (纸本)9798350309805

Reinforcement learning holds a great promise of enabling bipedal walking in humanoid robots. However, despite encouraging recent results, training still requires significant amounts of time and resources, precluding fast iteration cycles of the control development. Therefore, faster training methods are needed. In this paper, we investigate a number of techniques for improving sample efficiency of on-policy actor-critic algorithms and show that a significant reduction in training time is achievable with a few straightforward modifications of the common algorithms, such as PPO and DeepMimic, tailored specifically towards the problem of bipedal walking. Action space representation, symmetry prior induction, and cliprange scheduling proved effective at reducing sample complexity by a factor of 4.5. These results indicate that domain-specific knowledge can be readily utilized to reduce training times and thereby enable faster development cycles in challenging robotic applications.

关键词： Training Legged locomotion Deep learning Humanoid robots Reinforcement learning Control systems Hardware

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Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?

arXiv

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arXiv 2024年

作者： Lin, Ziqin Li, Heng Li, Zinan Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Singapore Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Recent advancements in pre-trained large foundation models (LFM) have yielded significant breakthroughs across various domains, including natural language processing and computer vision. These models have been particularly impactful in the domain of medical diagnostic tasks. With abundant unlabeled data, an LFM has been developed for fundus images using the Vision Transformer (VIT) and a self-supervised learning framework. This LFM has shown promising performance in fundus disease diagnosis across multiple datasets. On the other hand, deep learning models have long been challenged by dataset quality issues, such as image quality and dataset bias. To investigate the influence of data quality on LFM, we conducted explorations in two fundus diagnosis tasks using datasets of varying quality. Specifically, we explored the following questions: Is LFM more robust to image quality? Is LFM affected by dataset bias? Can fine-tuning techniques alleviate these effects? Our investigation found that LFM exhibits greater resilience to dataset quality issues, including image quality and dataset bias, compared to typical convolutional networks. Furthermore, we discovered that overall fine-tuning is an effective adapter for LFM to mitigate the impact of dataset quality issues. Copyright © 2024, The Authors. All rights reserved.

关键词： Image quality

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Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos

arXiv

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arXiv 2023年

作者： Islam, Mohammad Aminul Xing, Wangzhi Zhou, Jun Gao, Yongsheng Paliwal, Kuldip K. School of Information and Communication Technology Griffith University Australia Department of Computer Science and Mathematics Bangladesh Agricultural University Mymensingh2202 Bangladesh Institute for Integrated and Intelligent Systems Griffith University Australia

Hyperspectral object tracking has recently emerged as a topic of great interest in the remote sensing community. The hyperspectral image, with its many bands, provides a rich source of material information of an object that can be effectively used for object tracking. While most hyperspectral trackers are based on detection-based techniques, no one has yet attempted to employ YOLO for detecting and tracking the object. This is due to the presence of multiple spectral bands, the scarcity of annotated hyperspectral videos, and YOLO’s performance limitation in managing occlusions, and distinguishing object in cluttered backgrounds. Therefore, in this paper, we propose a novel framework called Hy-Tracker, which aims to bridge the gap between hyperspectral data and state-of-the-art object detection methods to leverage the strengths of YOLOv7 for object tracking in hyperspectral videos. Hy-Tracker not only introduces YOLOv7 but also innovatively incorporates a refined tracking module on top of YOLOv7. The tracker refines the initial detections produced by YOLOv7, leading to improved object-tracking performance. Furthermore, we incorporate Kalman-Filter into the tracker, which addresses the challenges posed by scale variation and occlusion. The experimental results on hyperspectral benchmark datasets demonstrate the effectiveness of Hy-Tracker in accurately tracking objects across frames. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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POMDP-Guided Active Force-Based Search for Robotic Insertion

POMDP-Guided Active Force-Based Search for Robotic Insertion

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IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Chen Wang Haoxiang Luo Kun Zhang Hua Chen Jia Pan Wei Zhang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong Cheng Kar-Shun Robotics Institute Hong Kong University of Science and Technology Clear Water Bay Hong Kong

In robotic insertion tasks where the uncertainty exceeds the allowable tolerance, a good search strategy is essential for successful insertion and significantly influences efficiency. The commonly used blind search method is time-consuming and does not exploit the rich contact information. In this paper, we propose a novel search strategy that actively utilizes the information contained in the contact configuration and shows high efficiency. In particular, we formulate this problem as a Partially Observable Markov Decision Process (POMDP) with carefully designed primitives based on an in-depth analysis of the contact configuration's static stability. From the formulated POMDP, we can derive a novel search strategy. Thanks to its simplicity, this search strategy can be incorporated into a Finite-State-Machine (FSM) controller. The behaviors of the FSM controller are realized through a low-level Cartesian Impedance Controller. Our method is based purely on the robot's proprioceptive sensing and does not need visual or tactile sensors. To evaluate the effectiveness of our proposed strategy and control framework, we conduct extensive comparison experiments in simulation, where we compare our method with the baseline approach. The results demonstrate that our proposed method achieves a higher success rate with a shorter search time and search trajectory length compared to the baseline method. Additionally, we show that our method is robust to various initial displacement errors.

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Flattening Singular Values of Factorized Convolution for Medical Images

Flattening Singular Values of Factorized Convolution for Med...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zexin Feng Na Zeng Jiansheng Fang Xingyue Wang Xiaoxi Lu Heng Meng Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Guangzhou Native-Stone Intelligent-Brain Technology Co. Ltd. Guangzhou China Department of Neurology The First Affiliated Hospital of Jinan University Guangzhou China

Convolutional neural networks (CNNs) have long been the paradigm of choice for robust medical image processing (MIP). Therefore, it is crucial to effectively and efficiently deploy CNNs on devices with different computing capabilities to support computer-aided diagnosis. Many methods employ factorized convolutional layers to alleviate the burden of limited computational resources at the expense of expressiveness. To this end, given weak medical image-driven CNN model optimization, a Singular value equalization generalizer-induced Factorized Convolution (SFConv) is proposed to improve the expressive power of factorized convolutions in MIP models. We first decompose the weight matrix of convolutional filters into two low-rank matrices to achieve model reduction. Then minimize the KL divergence between the two low-rank weight matrices and the uniform distribution, thereby reducing the number of singular value directions with significant variance. Extensive experiments on fundus and OCTA datasets demonstrate that our SFConv yields competitive expressiveness over vanilla convolutions while reducing complexity.

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MF-MOS: A Motion-Focused Model for Moving Object Segmentation

arXiv

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arXiv 2024年

作者： Cheng, Jintao Zeng, Kang Huang, Zhuoxu Tang, Xiaoyu Wu, Jin Zhang, Chengxi Chen, Xieyuanli Fan, Rui The School of Electronic and Information Engineering South China Normal University Foshan528225 China The Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Internet of Things Engineering Jiangnan University Wuxi China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS. © 2024, CC BY.

关键词： Semantics

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