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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Viral Rasik Galaiya Thiago Eustaquio Alves De Oliveira Xianta Jiang Vinicius Prado Da Fonseca Department of Computer Science Robotics and AI Lab Memorial University of Newfoundland and Labrador St. John’s Canada Department of Computer Science Ubiquitous Computing and Machine Learning Lab Memorial University of Newfoundland and Labrador St. John’s Canada Department of Computer Science Haptics and Robots Research Group Lakehead University Thunder Bay Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Object grasping is a complex task that requires high environmental awareness. While vision generally provides highly detailed environmental information, light changes, object transparency, camera resolution, and other factors such as occlusion and clutter affect its perception of object pose. Due to these limitations, there may be some deviation between the estimated and actual object pose in unstructured environments. The use of compliant tactile sensors relaxes the requirement of strict finger position planning while providing essential information regarding contact with the target object. Therefore, under positional uncertainty, the robotic system may use compliant tactile sensors to perform multiple attempts before a successful grasp. In the present paper, we investigate using reinforcement learning and compliant tactile sensors to provide adaptive grasping under pose uncertainty. Here, we identify a policy that models an object position estimation error while minimizing the exploratory sensor contact before obtaining a grasp. Our method was able to perform a successful grasp while reducing the number of attempts from an average of five to an average of two per episode.

关键词： Estimation error Uncertainty Contacts Fingers Tactile sensors Reinforcement learning Grasping

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MoSS: Monocular Shape Sensing for Continuum Robots

arXiv

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arXiv 2023年

作者： Shentu, Chengnan Li, Enxu Chen, Chaojun Dewi, Puspita Triana Lindell, David B. Burgner-Kahrs, Jessica Department of Computer Science University of Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Canada Continuum Robotics Laboratory Department of Mathematical and Computational Sciences University of Toronto Canada Waabi Canada

Continuum robots are promising candidates for interactive tasks in medical and industrial applications due to their unique shape, compliance, and miniaturization capability. Accurate and real-time shape sensing is essential for such tasks yet remains a challenge. Embedded shape sensing has high hardware complexity and cost, while vision-based methods require stereo setup and struggle to achieve real-time performance. This paper proposes a novel eye-to-hand monocular approach to continuum robot shape sensing. Utilizing a deep encoder-decoder network, our method, MoSSNet, eliminates the computation cost of stereo matching and reduces requirements on sensing hardware. In particular, MoSSNet comprises an encoder and three parallel decoders to uncover spatial, length, and contour information from a single RGB image, and then obtains the 3D shape through curve fitting. A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0.91 mm, or 0.36% of robot length) and real-time (70 fps) shape sensing on real-world data. Additionally, the method is optimized end-to-end and does not require fiducial markers, manual segmentation, or camera calibration. Code and datasets will be made available at https://github. com/Continuumroboticslab/MoSSNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Curve fitting

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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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A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models

One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large ...

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

作者： Hang Shao Bei Liu Yanmin Qian Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Various Large Language Models (LLMs) from the Generative Pretrained Transformer (GPT) family have achieved outstanding performances in a wide range of text generation tasks. However, the enormous model sizes have hindered their practical use in real-world applications due to high inference latency. Therefore, improving the efficiencies of LLMs through quantization, pruning, and other means has been a key issue in LLM studies. In this work, we propose a method based on Hessian sensitivity-aware mixed sparsity pruning to prune LLMs to at least 50% sparsity without the need of any retraining. It allocates sparsity adaptively based on sensitivity, allowing us to reduce pruning-induced error while maintaining the overall sparsity level. The advantages of the proposed method exhibit even more when the sparsity is extremely high. Furthermore, our method is compatible with quantization, enabling further compression of LLMs.

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Optimizing Moving Target Defense For Cyber Anomaly Detection 1

Optimizing Moving Target Defense For Cyber Anomaly Detection

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1st International Conference on computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Vubangsi, M. Mubarak, Auwalu Saleh Yayah, Jameel Altrjman, Chadi Manwal, Manika Yadav, Satya Prakash Al-Turjman, Fadi Research Center for Ai and IoT Ai and Robotics Institute Near East University Ai Engineering Dep't Mersin10 Turkey Htttc Bambili University of Bamenda Computational Materials Science Lab NWR Bambili Cameroon Waterloo University Canada GraphicEra Hill University Computer Science and Engineering Dehradun India Affiliated to Aktu Greater Noida India University of Kyrenia Faculty of Engineering Mersin10 Turkey

ISBN: (纸本)9798350338027

In this research, we evaluate the effectiveness of different MTD techniques on the transformer-based cyber anomaly detection models trained on the KDD Cup'99 Dataset, a publicly available dataset commonly used for evaluating intrusion detection systems. We explore the trade-offs between security and performance when using MTD techniques for cyber anomaly detection and investigate how MTD techniques can be combined with other cybersecurity techniques to improve the overall security of the system. We evaluate their performance using standard metrics such as accuracy and FI score, as well as measures of robustness against adversarial attacks. Our results show that MTD techniques can significantly improve the security of the anomaly detection model, with some techniques being more effective than others depending on the model architecture. We also find that there are trade-offs between security and performance, with some MTD techniques leading to a reduction in model accuracy or an increase in computation time. However, we demonstrate that these tradeoffs can be mitigated by optimizing the MTD parameters for the specific model architecture. © 2023 IEEE.

关键词： Cybersecurity

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Conversational Agents for Dementia using Large Language Models

Conversational Agents for Dementia using Large Language Mode...

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Mexican International Conference on computer science (ENC)

作者： Jesus Favela Dagoberto Cruz-Sandoval Mario O. Parra Computer Science Department CICESE Ensenada MEXICO Healthcare Robotics Lab University of California San Diego San Diego USA

Conversational robots are a type of social robot that emphasize verbal communication. One of their main application areas centers on assisting people with dementia and their caregivers, particularly in dealing with problematic behaviors. Recent advances in Large Language Models (LLMs) have increased their potential in developing conversational assistive agents. In this paper, we explore the use of a LLM (ChatGPT) to conduct cognitive stimulation therapies with people with dementia. We compare a conversational robot which includes personalized hand-coded conversational strategies with one using an LLM and prompt engineering. We conclude that the use of LLMs produces engaging conversations and has the potential to facilitate caregiver customization of social robots for dementia.

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Quantum Exploration-based Reinforcement Learning for Efficient Robot Path Planning in Sparse-Reward Environment

Quantum Exploration-based Reinforcement Learning for Efficie...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Chao Huang Yibei Guo Zhihui Zhu Mei Si Daniel Blankenberg Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Ohio State University Columbus OH USA Department of Cognitive Science Rensselaer Polytechnic Institute Troy NY USA Center for Computational Life Sciences Cleveland Clinic Cleveland OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

With the latest developments in sensors, battery, and Artificial Intelligence (AI) technologies, robots can perform missions in unstructured environments (e.g., disaster sites). However, their adaptation speed is still slow due to their limited onboard computing capability and dynamic disruption from environments, leading to inaccurate planning and delayed emergency reactions. Even though reinforcement learning increases robots’ exploration speed by fusing effective guidance, the numerous interactions make the learning time-consuming and even risky (e.g., collisions). To fundamentally improve robot adaptation speed, this work seeks help from quantum power. A novel Quantum Exploration based Dreamer model (QED) was developed to facilitate reinforcement learning explorations. QED based on stochastic quantum walker quickly explores environments, evaluates action quality, and obtains a global optimal exploration strategy; then, these high-quality exploration samples will be used to facilitate reinforcement learning speed. A theoretical benefit of QED is facilitating reinforcement learning speed without changing the underlying learning architecture, which makes the proposed QED applicable to general robot learning scenarios. To validate QED effectiveness, a robot path-planning task in an obstacle-dense environment was designed. The number of needed training episodes validated the effectiveness. The results show that QED achieves around ten times faster policy learning compared with Monte Carlo tree-based reinforcement learning methods and vanilla reinforcement learning methods.

关键词： Training Quantum mechanics Stochastic processes Reinforcement learning Robot sensing systems Robot learning Collision avoidance Artificial intelligence Quantum circuit Robots

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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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