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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Liu, Youfu Chen, Chunjie Lu, Jun Liu, Yida Zhang, Yu Wang, Zhuo College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction 23

Towards an Interaction Architecture for the iCub Robot: Soci...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Shaghaghi, Sahand Aliasghari, Pourya Tripp, Bryan Anderson, Britt Dautenhahn, Kerstin Nehaniv, Chrystopher L. Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Dept. of Psychology University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. WaterlooONN2L 3G1 Canada

ISBN: (纸本)9798350373578

Gaze behaviour plays a crucial role in social interactions extending to Human-Robot Interactions. Humanoid robots equipped with anthropomorphic vision systems may be expected to employ natural gaze behaviour in interactions with humans. Here, we implemented a novel first-person human gaze state detection tool on the iCub robot and assessed its gaze detection accuracy. Using this tool, we then developed two social gaze interaction architectures for the iCub robot. The Social Gaze Space theory (SGS) of Jording and colleagues was the theoretical framework for building our first architecture, SGS Base (SGS-B). Our second architecture, SGS Interaction Architecture (SGS-IA), is the extended version of SGS Base that lets the robot take on different higher-level goals in an interaction, including being attentive to the interaction partner or attempting to direct their attention to an object. These architectures support dyadic social interactions involving initiating and responding to joint attention towards objects of interest, and are expected to allow the robot to interact in a more human-like manner in social interactions. The gaze detection accuracy of the iCub robot equipped with our choice of algorithms exceeded that of previously implemented methods. System validation trials confirmed that the robot detects the social gaze states correctly in the majority of the instances, allowing for behavioural control according to the Social Gaze Space theory. Validation tests presented in this article demonstrate the functionality of the SGS Interaction Architecture and also highlight the differences in personal interaction dynamics when the humanoid robot has no particular goals as opposed to when it has interactional goals appropriate to a teaching scenario. © 2024 IEEE.

关键词： Anthropomorphic robots

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Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines

Look and Listen: A Multi-modality Late Fusion Approach to Sc...

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2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Jordan J. Bird Diego R. Faria Cristiano Premebida Anikó Ekárt George Vogiatzis School of Engineering and Applied Science Aston University Birmingham United Kingdom Aston Robotics Vision and Intelligent Systems (ARVIS) Lab Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16,000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach.

关键词： Image analysis Neural networks Urban areas Forestry Generators Rivers intelligent robots

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Goal Density-based Hindsight Experience Prioritization for Multi-Goal Robot Manipulation Reinforcement Learning

Goal Density-based Hindsight Experience Prioritization for M...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yingyi Kuang Abraham Itzhak Weinberg George Vogiatzis Diego R. Faria Aston Robotics Vision and Intelligent Systems Lab (ARVIS) School of Engineering and Applied Science Aston University Birmingham UK

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Reinforcement learning for multi-goal robot manipulation tasks is usually challenging, especially when sparse rewards are provided. It often requires millions of data collected before a stable strategy is learned. Recent algorithms like Hindsight Experience Replay (HER) have accelerated the learning process greatly by replacing the original desired goal with one of the achieved points (substitute goals) alongside the same trajectory. However, the selection of previous experience to learn is naively sampled in HER, in which the trajectory selection and the substitute goal sampling is completely random. In this paper, we discuss an experience prioritization strategy for HER that improves the learning efficiency. We propose the Goal Density-based hindsight experience Prioritization (GDP) method that focuses on utilizing the density distribution of the achieved points and prioritizes achieved points which are rarely seen in the replay buffer. These points are used as substitute goals for HER. In addition, we propose an Prioritization Switching with Ensembling Strategy (PSES) method to switch different experience prioritization algorithms during learning, which allows to select the best performance during each learning stage. We evaluate our method with several OpenAI Gym robotic manipulation tasks. The results show that GDP accelerates the learning process in most tasks and can be improved when combining with other prioritization methods using PSES.

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Deep learning in brain computer interfaces 19

Deep learning in brain computer interfaces

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9th Balkan Conference in Informatics, BCI 2019

作者： Georgieva, Petia University of Aveiro Department of Electronics Telecommunications and Informatics Institute of Electronics and Informatics Engineering of Aveiro -Intelligent Robotics and Systems Lab Aveiro Portugal

ISBN: (纸本)9781450371933

Deep Learning (DL) refers to computational models composed of multiple processing layers capable of learning representations of data with multiple levels of abstraction [1]. Since the seminal work of Lecun et al. [2] on training convolutional networks using back-propagation and gradient-based optimization, DL has outperformed other machine learning techniques in many domains of science and industry. Hinton et al. [3], achieved breakthrough on the task of automatic speech recognition, the first major DL industrial application. DL has achieved great success in recognition tasks within a wide range of applications including computer vision (images, videos), natural language processing (text), bioinformatics, to mention a few. © 2019 Association for Computing Machinery.

关键词： Convolution

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Automatic Context-Driven Inference of Engagement in HMI: A Survey

arXiv

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arXiv 2022年

作者： Salam, Hanan Celiktutan, Oya Gunes, Hatice Chetouani, Mohamed SMART Lab New York University Abu Dhabi United Arab Emirates Department of Engineering King’s College LondonWC2R 2LS United Kingdom Department of Computer Science and Technology University of Cambridge CB3 0FD United Kingdom Institute of Intelligent Systems and Robotics CNRS UMR7222 Sorbonne University Paris France

An integral part of seamless human-human communication is engagement, the process by which two or more participants establish, maintain, and end their perceived connection. Therefore, to develop successful human-centered human-machine interaction applications, automatic engagement inference is one of the tasks required to achieve engaging interactions between humans and machines, and to make machines attuned to their users, hence enhancing user satisfaction and technology acceptance. Several factors contribute to engagement state inference, which include the interaction context and interactants’ behaviours and identity. Indeed, engagement is a multi-faceted and multi-modal construct that requires high accuracy in the analysis and interpretation of contextual, verbal and non-verbal cues. Thus, the development of an automated and intelligent system that accomplishes this task has been proven to be challenging so far. This paper presents a comprehensive survey on previous work in engagement inference for human-machine interaction, entailing interdisciplinary definition, engagement components and factors, publicly available datasets, ground truth assessment, and most commonly used features and methods, serving as a guide for the development of future human-machine interaction interfaces with reliable context-aware engagement inference capability. An in-depth review across embodied and disembodied interaction modes, and an emphasis on the interaction context of which engagement perception modules are integrated sets apart the presented survey from existing surveys. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Low-cost Scalable People Tracking System for Human-Robot Collaboration in Industrial Environment

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Procedia Manufacturing 2020年 51卷 116-124页

作者： Matteo Terreran Edoardo Lamon Stefano Michieletto Enrico Pagello Intelligent Autonomous Systems Laboratory (IAS-Lab) Dept. of Information Engineering (DEI) University of Padova Padova Italy Robotics and Automation Dept. of Information Engineering Universitä degli Studi di Pisa Pisa Italy

Human-robot collaboration is one of the key elements in the Industry 4.0 revolution, aiming to a close and direct collaboration between robots and human workers to reach higher productivity and improved ergonomics. The first step toward such kind of collaboration in the industrial context is the removal of physical safety barriers usually surrounding standard robotic cells, so that human workers can approach and directly collaborate with robots. Anyway, human safety must be granted avoiding possible collisions with the robot. In this work, we propose the use of a people tracking algorithm to monitor people moving around a robot manipulator and recognize when a person is too close to the robot while performing a task. The system is implemented by a camera network system positioned around the robot workspace, and thoroughly evaluated in different industry-like settings in terms of both tracking accuracy and detection delay.

关键词： People tracking human-robot collaboration low-cost industrial

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Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM

Enhanced Visual Feedback with Decoupled Viewpoint Control in...

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IEEE-RAS International Conference on Humanoid Robots

作者： Yang Chen Leyuan Sun Mehdi Benallegue Rafael Cisneros-Limón Rohan P. Singh Kenji Kaneko Arnaud Tanguy Guillaume Caron Kenji Suzuki Abderrahmane Kheddar Fumio Kanehiro School of Integrative and Global Majors (SIGMA) University of Tsukuba Japan CNRS-AIST JRL (Joint Robotics Laboratory) IRL National Institute of Advanced Industrial Science and Technology (AIST) Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Japan MIS lab Universite de Picardie Jules Verne Amiens France Faculty of Engineering and Center for Cybernetics Research University of Tsukuba Japan

ISBN: (数字)9798350309799

ISBN: (纸本)9798350309805

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: (i) the lag between the motion of the operator's and robot's head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle; (ii) the mismatch between the camera's and the headset's field-of-views (FOV), the former having generally a lower FOV; and (iii) a mismatch between human's and robot's range of motions of the neck, the latter being also generally lower. In order to leverage these draw-backs, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera's FOV range to match that of the operator's headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot's head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows that the effectiveness of our solution.

关键词： Point cloud compression Visualization Simultaneous localization and mapping Humanoid robots Virtual reality Real-time systems Robustness

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Towards an Interaction Architecture for the iCub Robot: Social Gaze Space Model Adaptation for Social Interaction

Towards an Interaction Architecture for the iCub Robot: Soci...

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IEEE-RAS International Conference on Humanoid Robots

作者： Sahand Shaghaghi Pourya Aliasghari Bryan Tripp Britt Anderson Kerstin Dautenhahn Chrystopher L. Nehaniv Social & Intelligent Robotics Research Lab University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Systems Design Enginering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Electrical & Computer Engineering University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Centre for Theoretical Neuroscience University of Waterloo 200 University Ave. W. Waterloo Ontario Canada Dept. of Psychology University of Waterloo 200 University Ave. W. Waterloo Ontario Canada

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

关键词： Accuracy Architecture Education Humanoid robots Human-robot interaction Modulation Aerospace electronics Visual systems System validation Standards

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