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Simultaneous tactile exploration and grasp refinement for unknown objects

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

The privacy paradox and optimal bias-variance trade-offs in data acquisition

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arXiv 2021年

作者： Liao, Guocheng Su, Yu Ziani, Juba Wierman, Adam Huang, Jianwei Department of Information Engineering The Chinese University of Hong Kong Department of Computing and Mathematical Sciences California Institute of Technology United States Department of Computer and Information Science University of Pennsylvania United States Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen

While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised;the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive compatible mechanism that optimizes the worst-case trade-off between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace. Copyright © 2021, The Authors. All rights reserved.

关键词： Data acquisition

Constraint Selection in Optimization-Based Controllers

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arXiv 2025年

作者： Lee, Haejoon Rousseas, Panagiotis Panagou, Dimitra Department of Robotics University of Michigan Ann ArborMI United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Aerospace Engineering University of Michigan Ann ArborMI United States

Human-machine collaboration often involves constrained optimization problems for decision-making processes. However, when the machine is a dynamical system with a continuously evolving state, infeasibility due to multiple conflicting constraints can lead to dangerous outcomes. In this work, we propose a heuristic-based method that resolves infeasibility at every time step by selectively disregarding a subset of soft constraints based on the past values of the Lagrange multipliers. Compared to existing approaches, our method requires the solution of a smaller optimization problem to determine feasibility, resulting in significantly faster computation. Through a series of simulations, we demonstrate that our algorithm achieves performance comparable to state-of-the-art methods while offering improved computational efficiency. © 2025, CC BY.

关键词： Lagrange multipliers

Automatic vocabulary and graph verification for accurate loop closure detection

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arXiv 2021年

作者： Yue, Haosong Miao, Jinyu Chen, Weihai Wang, Wei Guo, Fanghong Li, Zhengguo The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom The Department of Automation Zhejiang University of Technology Hangzhou China The Robotics Department Institute for Infocomm Research 138632 Singapore

Localizing pre-visited places during long-term simultaneous localization and mapping, i.e. loop closure detection (LCD), is a crucial technique to correct accumulated inconsistencies. As one of the most effective and efficient solutions, Bag-of-Words (BoW) builds a visual vocabulary to associate features and then detect loops. Most existing approaches that build vocabularies off-line determine scales of the vocabulary by trial-and-error, which often results in unreasonable feature association. Moreover, the accuracy of the algorithm usually declines due to perceptual aliasing, as the BoW-based method ignores the positions of visual features. To overcome these disadvantages, we propose a natural convergence criterion based on the comparison between the radii of nodes and the drifts of feature descriptors, which is then utilized to build the optimal vocabulary automatically. Furthermore, we present a novel topological graph verification method for validating candidate loops so that geometrical positions of the words can be involved with a negligible increase in complexity, which can significantly improve the accuracy of LCD. Experiments on various public datasets and comparisons against several state-of-the-art algorithms verify the performance of our proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Mapping

Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

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arXiv 2023年

作者： Kwon, Kangkyu Lee, Yoon Jae Jung, Yeongju Soltis, Ira Choi, Chanyeong Na, Yewon Romero, Lissette Kim, Myung Chul Rodeheaver, Nathan Kim, Hodam Lloyd, Michael S. Zhuang, Ziqing King, William Xu, Susan Ko, Seung-Hwan Lee, Jinwoo Yeo, Woon-Hong School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Human-Centric Interfaces and Nanoengineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States Applied Nano and Thermal Science Lab Department of Mechanical Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States Division of Cardiology Emory University School of Medicine AtlantaGA30322 United States National Institute for Occupational Safety and Health National Personal Protective Technology Laboratory PittsburghPA15236 United States Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University 30 Pildongro 1-gil Jung-gu Seoul04620 Korea Republic of Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user’s face, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the *** Codes 92C55 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

Optimization Model of Housing Construction Project Scheduling Based on Optimized Ant Colony Algorithm

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Optimization Model of Housing Construction Project Schedulin...

International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Srinivasa Gowda G K K. Sambath Kumar G R Sanjay Krishna Madhan Mohan Kumar J. Rex N. Manikandan Department of Computer Science and Engineering ACS College of Engineering Bangalore Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department: Mechanical Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh India Department of Robotics and Automation Easwari Engineering College Bharathi Salai Ramapuram Chennai Tamilnadu India Department of Civil Engineering Malla Reddy Engineering College Hyderabad Tamilnadu India Dept of Mechanical Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

Construction management is a standardized means of project management. The construction project takes a long time and has many links, and the construction workers involved are complicated. Poor quality buildings will not only affect the construction enterprises’ brand, but also threaten the safety of people's lives and property. In this paper, an optimization model of housing construction project scheduling based on ant colony algorithm (ACA) is put forward. By studying the relationship among various sub-projects in the construction system of building projects, the management of building projects can be better realized. The results show that compared with the other two algorithms, the optimized ACA has certain advantages in recall and accuracy. There are many contents involved in the building construction process, and there are many uncontrollable factors in the building process, which greatly impact the project's construction quality. The construction industry can use this technology to strengthen the building construction management, solve the problems existing in the construction management, and improve the construction quality

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Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

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arXiv 2020年

作者： Ferrarini, Bruno Milford, Michael McDonald-Maier, Klaus D. Ehsan, Shoaib School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom QUT Centre for Robotics School of Electrical Engineering and Robotics Australian Centre for Robotic Vision Queensland University of Technology Centre for Robotics Australia

Visual place recognition (VPR) is a robot’s ability to determine whether a place was visited before using visual data. While conventional hand-crafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this paper, we take a multi-step approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the visual place recognition problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed seven times. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

Balance Assessment for Double and Single Leg Stance Using FIBOD Balance System 3rd

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Balance Assessment for Double and Single Leg Stance Using FI...

3rd International Conference for Innovation in Biomedical Engineering and Life sciences, ICIBEL 2020

作者： Khor, Kang Xiang Yeong, Che Fai Su, Eileen Lee Ming Latip, Hadafi Fitri Mohd Khor, Yvonne Yee Woon Chan, Sook Chin Chern, Phei Ming Leow, Mei Lian Abdullah, Najib bin Mustar, Muhammad Farhan bin Halim, Muhammad Nurhakim bin Abdul Skudai Johor81300 Malaysia Center of Artificial Intelligence and Robotics UTM Kuala Lumpur54100 Malaysia School of Biomedical Engineering and Health Science Faculty of Engineering UTM Skudai Johor81310 Malaysia Sports Innovation and Technology Centre UTM Skudai Johor81300 Malaysia UTM Skudai Johor81310 Malaysia MAHSA University Jenjarom Selangor42610 Malaysia Clinical Research Centre Cheras Rehabilitation Hospital Kuala Lumpur56000 Malaysia Faculty of Computer and Mathematical Sciences UiTM Segamat85000 Malaysia

ISBN: (纸本)9783030650919

Physical balance is important to support human motion for almost all activities of daily living such as standing, sitting, cooking, housework or shopping. However, balance tends to drop as human grow older due to the loss of muscle. FIBOD balance system (FBS) is a balance system designed to be portable and easy to use. This study investigates the FBS balance measures with 17 healthy subjects. Results show significant differences in double-leg overall stability index (OSI) and medial-lateral stability index (MLSI) (p = 0.01);double-leg and dominant leg MLSI (p © 2021, Springer Nature Switzerland AG.

关键词： Balance assessment Balance board Instrumented wobble board

Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation"

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IEEE transactions on cybernetics 2024年第11期54卷 7116页

作者： Lei Zhou Liang Feng Kay Chen Tan Jinghui Zhong Zexuan Zhu Kai Liu Chao Chen Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China Department of Computer Science City University of Hong Kong kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Presents corrections to the paper, (Corrections to "Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation").

关键词： Evolutionary computation Knowledge transfer Cybernetics