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arXiv 2023年

作者： Li, Tianan Chen, Zhehan Liu, Huan Wang, Chen The School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China The Department of Electrical and Computer Engineering McMaster University Canada The Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain computationally intensive and often fail to meet the real-time requirements of low-power robotic platforms. Additionally, most methods are designed for opaque objects and struggle with transparent objects due to the special properties of reflection and refraction. To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation. To leverage local information and avoid overfitting issues when integrating it with global information, we design a new fusion branch and shortcuts to exploit low-level features and a loss function to suppress overfitting. This results in an accurate and user-friendly depth rectification framework which can recover dense depth estimation from RGB-D images alone. Extensive experiments demonstrate that FDCT can run about 70 FPS with a higher accuracy than the state-of-the-art methods. We also demonstrate that FDCT can improve pose estimation in object grasping tasks. The source code is available at https://***/Nonmy/FDCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Robots in education: Influence of Regulatory Focus Theory

Robots in education: Influence of Regulatory Focus Theory

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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

The Covid-19 pandemic has massively developed the use of distance learning. The limits of this practice have gradually come to light, both for students and for teachers. It is now crucial to design alternative solutions to overcome the shortcomings of videoconferencing in terms of involvement, concentration, learning, and equity. Social robots are increasingly used as tutors in the educational context and help improve teaching efficiency. Many psychology-based principles have been applied in education to guide instructional strategies, motivate students, and create a positive and productive learning environment. In this work, we use Regulatory Focus Theory (RFT), which categorizes an individual’s motivation into two types: Promotion and Prevention. Promotion-focused individuals are motivated by the potential for growth and achievement, whereas prevention-focused individuals are motivated by the potential for avoiding negative outcomes. Based on RFT, we aim to explore if and how the regulatory-focused behavior of the tutor robot can affect participants’ learning outcomes. In this work, a language learning scenario was designed with two conditions: (1) a robot tutor with promotion-focused behavior, (2) a robot tutor with prevention-focused behavior. The results are encouraging and support that promotion robot tutor can increase the learning efficiency of promotion participants and prevention robot tutor will enhance the learning interest of prevention participants.

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Learning Inverse Kinematics Multiplicity of Concentric Tube Robots Using Invertible Neural Networks

Learning Inverse Kinematics Multiplicity of Concentric Tube ...

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International Symposium on Medical robotics (ISMR)

作者： Paul H. Kang Radian Gondokaryono Majid Roshanfar Robert H. Nguyen Thomas Looi James M. Drake Dale Podolsky Institute of Biomedical Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Department of Computer Science Medical Computer Vision and Robotics Lab University of Toronto Canada

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Concentric tube robots (CTR) are a promising technology for medical applications due to their small size, flexibility, and ability to make complex shapes. These robots are built from a series of pre-curved, super-elastic tubes that are arranged concentrically and manipulated through rotational and translational movements at their proximal ends. Achieving accurate kinematics is essential in making CTRs useful in minimally invasive surgical procedures where precision and safety is paramount. Due to the difficulty of incorporating nonlinear effects like friction and tube clearances into analytical models, previous works have investigated machine learningbased models for CTR kinematics, leading to higher kinematic accuracies. We present a kinematic model for CTRs using an invertible neural network architecture that, unlike other learningbased models, can generate multiple inverse kinematic solutions. Our model achieved a mean forward kinematic tip error of 2.86 mm ( $\mathbf{3. 4 3 \%}$ normalized to arclength), outperforming a Cosserat rod-based analytical CTR model. In three test cases (two static points and a circle trajectory), our model achieved mean inverse kinematic errors of $6.19,3.33$ , and $3.86 ~\text{mm}(5.66 \%, 4.12 \%$ , and 4.16 % normalized to arclength). We additionally present a robust data capture pipeline that is able to reconstruct the CTR's shape that uses state-of-the-art segmentation models.

关键词： Analytical models Concentric tube robots Accuracy Minimally invasive surgery Shape Neural networks Pipelines Kinematics Trajectory Safety

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Towards the Influence of Human Observer Eye-Movements on Discriminating Between Perceived Sociability of Different Robots 54

Towards the Influence of Human Observer Eye-Movements on Dis...

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54th International Symposium on robotics, ISR Europe 2022

作者： Kühnlenz, Kolja Kühnlenz, Barbara Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and Arts CoburgD-96450 Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts CoburgD-96450 Germany

ISBN: (纸本)9783800758920

Results from a study on Perceived Sociability of a robot dependent on different gaze behaviors of human observers are presented. A 2x2 between-subjects design is used with independent variables’gaze-behavior’ (static/dynamic) and’robot’ (appearance) of a displayed robot head. Participants are told to fixate a point on a screen, which is either static or moving linearly, while a picture of one of two robot heads is shown in the covert visual attention region of the human observer. The robots differ in the level of human-likeness (mechanistic vs. anthropomorphic). Significant results show, that Perceived Sociability depends on the interaction of gaze-behavior and robot appearance. The findings show, that eye-movements influence covert perception of robot attributes and contribute to a better understanding of gaze dependent perception of robot appearance with potential practical implications of situational perception of anthropomorphic attributes like sociability situationally impacting interaction with the robot. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Eye movements

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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels

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IEEE Access 2025年 13卷 89542-89555页

作者： Rahiman, Wan Ghazali, Mohamad Hazwan Mohd Mahmood, Iskandar Al-Thani Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang Nibong Tebal14300 Malaysia Daffodil International University Daffodil Robotics Lab Department of Computer Science and Engineering Dhaka1216 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Mechanical and Manufacturing Engineering Selangor Bangi43600 Malaysia PETRONAS Research Sdn Bhd Selangor Kajang43000 Malaysia

Advancement in marine vessel stabilization technologies has greatly improved the safety and comfort for crews, cargo, and passengers, while also lowering the risk of capsizing. Marine vessels operating in open waters experience disturbances from unpredictable waves and strong winds, leading to instability and potential accidents. To mitigate these risks, stabilizers are integrated into ships to maintain stability, safety, and seaworthiness. As modern vessels evolve, stabilizers play an increasingly vital role, with gyro stabilizers emerging as a highly effective solution for mitigating roll motion without external appendages. This paper provides a comprehensive review of gyro stabilizer designs and techniques, covering their historical development, mathematical modeling, advantages, limitations, and case studies. A review of current research highlights a strong emphasis on simulating gyro stabilizer control systems, demonstrating their potential in enhancing vessel stability. However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. By exploring the latest developments, this review serves as a valuable reference for researchers, engineers, and maritime professionals seeking to advance marine stabilization technologies. The findings underscore the potential of next-generation gyro stabilizers, including fiber optic gyros, in achieving higher precision, stability, and operational efficiency. © 2013 IEEE.

关键词： Warships

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct Shooting Method for Numerical Optimal Control: A Modi...

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European Control Conference (ECC)

作者： Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.

关键词： Upper bound Accuracy System dynamics Optimal control Europe Programming Benchmark testing

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Enhancing Post-Stroke Upper Limb Rehabilitation Through Haptic Feedback in Virtual Reality-Based Gaming

Enhancing Post-Stroke Upper Limb Rehabilitation Through Hapt...

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IEEE International Conference on robotics and Biomimetics

作者： Branesh M. Pillai Napas Siripala Kongtup Sai-Aroon Dileep Sivaraman Ramana Vinjamuri Sorayouth Chumnanvej Jackrit Suthakorn Department of Biomedical Engineering Faculty of Engineering Center for Biomedical and Robotics Technology (BART LAB) Mahidol University Thailand Vinjamuri lab in the Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD USA Department of Surgery Neurosurgery Division Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok Thailand

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

This preliminary study explores developing and implementing upper-limb rehabilitation tools utilizing haptic feedback and 3D spatial recognition analysis in a virtual reality (VR) environment. The effectiveness of upper limb rehabilitation in post-stroke patients was investigated by analyzing the joint and gimbal range of motion (ROM), ROM ratios, and force distribution through various drawing tasks. This study evaluated the impact of different drawing patterns on joint and gimbal ROM, and the force applied during rehabilitation exercises. This study revealed significant variability in ROM across different tasks by combining ROM measurements with force data and employing heat maps for visual representation. These findings emphasize the need for task-specific adjustments to improve rehabilitation outcomes. This study highlights the potential of incorporating advanced technologies, such as VR-based tools with haptic feedback and 3D spatial recognition, to enhance patient engagement and rehabilitation effectiveness.

关键词： Limbs Visualization Three-dimensional displays Tracking Force Medical treatment Virtual reality Haptic interfaces Motion control Read only memory

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Byzantine Robust Aggregation in Federated Distillation with Adversaries

Byzantine Robust Aggregation in Federated Distillation with ...

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International Conference on Distributed Computing Systems

作者： Wenrui Li Hanlin Gu Sheng Wan Zhirong Luan Wei Xi Lixin Fan Qiang Yang Badong Chen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China WeBank AI Lab Shenzhen China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China School of Electrical Engineering Xi'an University of Technology Xi'an China Department of Computer Science Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated learning empowers privacy-preserving, multi-party secure model training without the necessity of sharing raw data. In recent years, knowledge distillation has emerged as a promising solution to address the significant challenge of model heterogeneity within federated learning. However, current research often overlooks the potential threats posed by Byzantine attacks, which can significantly compromise the security of federated distillation. Previous work on Byzantine attacks has been primarily focused on manipulating local gradients to compromise global model, lacking attacks on logits in knowledge distillation scenarios. In this paper, we introduce two innovative attacks, shedding light on the inherent risks in federated distillation. The proposed attacks include a top-k attack, which perturbs the top k values of logits in each column, and an impersonation attack, which emulates knowledge significantly deviating from the norm. To counter such attacks, we propose a robust aggregation strategy-FedTGD (Federated Top Guard Distillation), designed to ensure robust distillation with heterogeneous models. Specifically, FedTGD incorporates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and maximum cosine similarity on top-k values of logits to select benign knowledge. Experimental evaluations conducted on FEMNIST and CIFAR100 datasets, considering scenarios for both IID and Non-IID, reveal that top-k attack results in a substantial 27.16% accuracy reduction for FedMD. In contrast, our aggregation method shows a marginal 0.7% accuracy decrease under top-k attacks, outperforming state-of-the-art baselines.

关键词： Training Accuracy Federated learning Computational modeling Noise Impersonation attacks Data models

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

arXiv

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arXiv 2024年

作者： Tang, Jiawei Zhong, Yuxing Wang, Pengyu Chen, Xingzhou Wu, Shuang Shi, Ling The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Noah’s Ark Lab Huawei Canada

关键词： Nonlinear programming

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A Distributed Deep Learning Approach for A Team of Unmanned Aerial Vehicles for Wildfire Tracking and Coverage

A Distributed Deep Learning Approach for A Team of Unmanned ...

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IEEE International Conference on Robotic Computing (IRC)

作者： Kripash Shrestha Hung Manh La Hyung-Jin Yoon Amazon Robotics Research Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno USA Department of Mechanical Engineering University of Nevada Reno USA

ISBN: (纸本)9781665472616

Recent large wildfires in the United States and the subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there need to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) have been previously used in this problem domain to track and monitor wildfires with approaches such as artificial potential fields and reinforcement learning. Our work aims to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments. We proposed and implemented the Deep Q-Network (DQN) with a state estimator (auto-encoder), then compared it to existing methods including a Q-learning, a Q-learning with experience replay, and a DQN. The proposed DQN with a state estimator outperformed existing deep learning methods in terms of reward maximization and convergence.

关键词： Deep learning Q-learning Computational modeling Fires Autonomous aerial vehicles Vehicle dynamics Task analysis

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