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arXiv 2023年

作者： Thach, Bao Watts, Tanner Ho, Shing-Hei Hermans, Tucker Kuntz, Alan Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States NVIDIA Corporation SeattleWA United States

Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method’s effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical real-world applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Demonstrations

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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The Classification of Kidney Disease: A Feature-Based Transfer Learning Approach 1

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11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Xu, Yifeng He, Haoyu Majeed, Anwar P. P. Abdul Musa, Rabiu Muazu Hu, Bintao Nasir, Ahmad Fakhri Ab. Yap, Eng Hwa School of Robotics Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China School of Artificial Intelligence and Advanced Computing Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Center for Fundamental and Continuing Education Department of Credited Co-Curriculum Universiti Malaysia Terengganu Kuala Nerus Terengganu Terengganu Malaysia Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (数字)9783031706875

ISBN: (纸本)9783031706868

Kidney disease is widespread and poses serious health risks. Therefore, early detection of kidney diseases is crucial for providing patients with timely medical care, thus improving their chances of survival. Conventional means of diagnosing is laborious and at times biased. Hence, computer aided diagnosis (CAD) has been promoted to facilitate this issue. This study investigates the ability of different feature-based transfer learning pipelines in its ability to classify four different types of kidney diseases from histopathological images of the tissues. The InceptionV3, VGG16 and VGG19 pre-trained convolutional neural network models were used to extract the features from the images, and consequently the fully connected layers of the pre-trained models were swapped with an instance-based classifier, namely k-Nearest Neighbors (kNN). It was shown from the study that the InceptionV3 + kNN pipeline was able to discriminate the different classes significantly well on both the validation and test dataset. This suggest that the proposed technique is an efficient means for classifying kidney diseases and a promising CAD approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach

Quadruped Guidance Robot for the Visually Impaired: A Comfor...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yanbo Chen Zhengzhe Xu Zhuozhu Jian Gengpan Tang Liyunong Yang Anxing Xiao Xueqian Wang Bin Liang School of Mechanical Engineering and Automation at Harbin Institute of Technology Shenzhen China Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen China School of Computing National University of Singapore Singapore Singapore

Guidance robots that can guide people and avoid various obstacles, could potentially be owned by more visually impaired people at a fairly low cost. Most of the previous guidance robots for the visually impaired ignored the human response behavior and comfort, treating the human as an appendage dragged by the robot, which can lead to imprecise guidance of the human and sudden changes in the traction force experienced by the human. In this paper, we propose a novel quadruped guidance robot system with a comfort-based concept. We design a controllable traction device that can adjust the length and force between human and robot to ensure comfort. To allow the human to be guided safely and comfortably to the target position in complex environments, our proposed human motion planner can plan the traction force with the force-based human motion model. To track the planned force, we also propose a robot motion planner that can generate the specific robot motion command and design the force control device. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios. (Video 1 1 Video demonstration: https://***/gd-RcYOqGuo.)

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Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

arXiv

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arXiv 2025年

作者： Chen, Qiguang Qin, Libo Liu, Jinhao Peng, Dengyun Guan, Jiannan Wang, Peng Hu, Mengkang Zhou, Yuhang Gao, Te Che, Wanxiang LARG China Research Center for Social Computing and Interactive Robotics China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The University of Hong Kong Hong Kong Fudan University China

Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like mathematics and coding. A central factor in their success lies in the application of long chain-of-thought (Long CoT) characteristics, which enhance reasoning abilities and enable the solution of intricate problems. However, despite these developments, a comprehensive survey on Long CoT is still lacking, limiting our understanding of its distinctions from traditional short chain-of-thought (Short CoT) and complicating ongoing debates on issues like "overthinking" and "test-time scaling." This survey seeks to fill this gap by offering a unified perspective on Long CoT. (1) We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms. (2) Next, we explore the key characteristics of Long CoT: deep reasoning, extensive exploration, and feasible reflection, which enable models to handle more complex tasks and produce more efficient, coherent outcomes compared to the shallower Short CoT. (3) We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling, offering insights into how these processes manifest in practice. (4) Finally, we identify significant research gaps and highlight promising future directions, including the integration of multi-modal reasoning, efficiency improvements, and enhanced knowledge frameworks. By providing a structured overview, this survey aims to inspire future research and further the development of logical reasoning in artificial intelligence1 © 2025, CC BY-SA.

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Automatic Recognition of Social Engagement for Children with Autism Spectrum Disorder

Automatic Recognition of Social Engagement for Children with...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Xinming Zhang, Xiangdong Wang, Zhiyong Nie, Wei Zhang, Hanlin Xu, Xiu Liu, Honghai Center of Medical Prenatal Diagnosis Lishui Maternity and Child Health Care Hospital Lishui323000 China Children's Hospital of Fudan University Department of Child Health Care Shanghai201102 China State Key Laboratory of Robotics and Systems Shenzhen518055 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9781665410205

Estimating children's engagement levels improves their understanding of their social behaviors, since they can reflect their devotion to social interaction with others. This paper proposes an automatic method to recognize children's engagement levels in a triadic social interaction context. First, an overall metric function containing behavior, cognition, and affective dimensions is proposed to estimate children's multidimensional engagement levels. Then, the automatic feature extraction method based on gaze estimation, facial expression recognition, pose estimation, and object recognition models is illustrated to extract features to compute the engagement levels. Videos of 24 children, including 13 children with autism spectrum disorder (ASD), in triadic social interaction were collected for the engagement recognition experiment and cross-group analysis. The experimental results validate the effectiveness of the proposed automatic feature extraction method compared to human observations. Cross-group analyses revealed significant differences in affective engagement between children with ASD and typical developmental (TD) children. © 2024 IEEE.

关键词： Diseases

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Distortion-Disentangled Contrastive Learning

Distortion-Disentangled Contrastive Learning

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jinfeng Wang Sifan Song Jionglong Su S. Kevin Zhou School of AIAC Xi’an Jiaotong-Liverpool University Suzhou China School of BME & Suzhou Institute for Advanced Research Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) University of Science and Technology of China Suzhou China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Self-supervised learning is well known for its remarkable performance in representation learning and various downstream computer vision tasks. Recently, Positive-pair-Only Contrastive Learning (POCL) has achieved reliable performance without the need to construct positive-negative training sets. It reduces memory requirements by lessening the dependency on the batch size. The POCL method typically uses a single objective function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions. This objective function implicitly enables the model to filter out or ignore the distortion variant representation (DVR) affected by different distortions. However, some recent studies have shown that proper use of DVR in contrastive can optimize the performance of models in some downstream domain-specific tasks. In addition, these POCL methods have been observed to be sensitive to augmentation strategies. To address these limitations, we propose a novel POCL framework named Distortion-Disentangled Contrastive Learning (DDCL) and a Distortion-Disentangled Loss (DDL). Our approach is the first to explicitly and adaptively disentangle and exploit the DVR inside the model and feature stream to improve the representation utilization efficiency, robustness and representation ability. Experiments demonstrate our framework’s superiority to Barlow Twins and Simsiam in terms of convergence, representation quality (including transferability and generalization), and robustness on several datasets.

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Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models

arXiv

引用

arXiv 2023年

作者： Huang, Yixuan Yuan, Jialin Kim, Chanho Pradhan, Pupul Chen, Bryan Fuxin, Li Hermans, Tucker Robotics Center Kahlert School of Computing University of Utah Salt Lake CityUT84112 United States Oregon State University United States NVIDIA SeattleWA United States

Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments. We investigate the problem of encoding object-oriented memory into a multi-object manipulation reasoning and planning framework. We propose DOOM and LOOM, which leverage transformer relational dynamics to encode the history of trajectories given partial-view point clouds and an object discovery and tracking engine. Our approaches can perform multiple challenging tasks including reasoning with occluded objects, novel objects appearance, and object reappearance. Throughout our extensive simulation and real-world experiments, we find that our approaches perform well in terms of different numbers of objects and different numbers of distractor actions. Furthermore, we show our approaches outperform an implicit memory baseline. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch

arXiv

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arXiv 2022年

作者： Matak, Martin Hermans, Tucker The School of Computing The Robotics Center University of Utah Salt Lake CityUT United States NVIDIA SeattleWA United States

Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many tasks including tool use, insertion, and dexterous in-hand manipulation. This task becomes even more difficult when the robot lacks an accurate model of the object to be grasped. Tactile sensing offers a promising approach to account for uncertainties in object shape. However, current robotic hands tend to lack full tactile coverage. As such, a problem arises of how to plan and execute grasps for multifingered hands such that contact is made with the area covered by the tactile sensors. To address this issue, we propose an approach to grasp planning that explicitly reasons about where the fingertips should contact the estimated object surface while maximizing the probability of grasp success. Key to our method's success is the use of visual surface estimation for initial planning to encode the contact constraint. The robot then executes this plan using a tactile-feedback controller that enables the robot to adapt to online estimates of the object's surface to correct for errors in the initial plan. Importantly, the robot never explicitly integrates object pose or surface estimates between visual and tactile sensing, instead it uses the two modalities in complementary ways. Vision guides the robots motion prior to contact;touch updates the plan when contact occurs differently than predicted from vision. We show that our method successfully synthesises and executes precision grasps for previously unseen objects using surface estimates from a single camera view. Further, our approach outperforms a state of the art multi-fingered grasp planner, while also beating several baselines we propose. © 2022, CC BY.

关键词： Robotic arms

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