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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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Detect-and-segment: A deep learning approach to automate wound image segmentation

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Informatics in Medicine Unlocked 2022年 29卷

作者： Scebba, Gaetano Zhang, Jia Catanzaro, Sabrina Mihai, Carina Distler, Oliver Berli, Martin Karlen, Walter Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zurich Switzerland Unit for Clinical and Applied Research Balgrist University Hospital Zurich Switzerland Department of Rheumatology University Hospital Zurich University of Zurich Switzerland Division of Technical Orthopedics Department of Orthopedic Surgery Balgrist University Hospital Zurich Switzerland

Chronic wounds significantly impact quality of life. They can rapidly deteriorate and require close monitoring of healing progress. Image-based wound analysis is a way of objectively assessing the wound status by quantifying important features that are related to healing. However, high heterogeneity of the wound types and imaging conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the perturbing background, and computed a wound segmentation map. We tested this approach on a diabetic foot ulcers data set and compared it to a segmentation method based on the full image. To evaluate its generalizability on out-of-distribution data, we measured the performance of the DS approach on 4 additional independent data sets, with larger variety of wound types from different body locations. The Matthews’ correlation coefficient (MCC) improved from 0.29 (full image) to 0.85 (DS) on the diabetic foot ulcer data set. When the DS was tested on the independent data sets, the mean MCC increased from 0.17 to 0.85. Furthermore, the DS enabled the training of segmentation models with up to 90% less training data without impacting the segmentation performance. The proposed DS approach is a step towards automating wound analysis and reducing efforts to manage chronic wounds. © 2022 The Authors

关键词： Chronic wounds Generalizability Machine learning Semantic segmentation Smartphone

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Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning

Deep Visual Heuristics: Learning Feasibility of Mixed-Intege...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Danny Driess Ozgur Oguz Jung-Su Ha Marc Toussaint Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Stuttgart Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the scene and goals can be encoded as input to the learning algorithm in a way that enables to generalize over a variety of tasks in environments with changing numbers of objects and goals. To achieve this, we propose to encode the scene and the target object directly in the image *** experiments show that our proposed network generalizes to scenes with multiple objects, although during training only two objects are present at the same time. By using the learned network as a heuristic to guide the search over the discrete variables of the mixed-integer program, the number of optimization problems that have to be solved to find a feasible solution or to detect infeasibility can greatly be reduced.

关键词： Planning Task analysis Robot sensing systems Neural networks Grasping Search problems

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An Interior Point Method Solving Motion Planning Problems with Narrow Passages

arXiv

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arXiv 2020年

作者： Mainprice, Jim Ratliff, Nathan Toussaint, Marc Schaal, Stefan Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPITübingen & Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany

Algorithmic solutions for the motion planning problem have been investigated for five decades. Since the development of A* in 1969 many approaches have been investigated, traditionally classified as either grid decomposition, potential fields or sampling-based. In this work, we focus on using numerical optimization, which is understudied for solving motion planning problems. This lack of interest in the favor of sampling-based methods is largely due to the non-convexity introduced by narrow passages. We address this shortcoming by grounding the solution in differential geometry. We demonstrate through a series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how modeling explicitly the underlying Riemannian manifold leads to an efficient interior point non-linear programming solution. 1. Copyright © 2020, The Authors. All rights reserved.

关键词： Nonlinear programming

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An Interior Point Method Solving Motion Planning Problems with Narrow Passages

An Interior Point Method Solving Motion Planning Problems wi...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Jim Mainprice Nathan Ratliff Marc Toussaint Stefan Schaal Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen & Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

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How to Model Brushless Electric Motors for the Design of Lightweight Robotic systems

arXiv

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arXiv 2023年

作者： Lee, Ung Hee Shepherd, Tor Kim, Sangbae De, Avik Su, Hao Gregg, Robert Mooney, Luke Rouse, Elliott Neurobionics Lab. University of Michigan Ann ArborMI48109 United States Department of Mechanical Engineering University of Michigan United States The Robotics Institute University of Michigan United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Lab of Biomechatronics and Intelligent Robotics Department of Mechanical Engineering and Aerospace Engineering North Carolina State University RaleighNC27695 United States Department of Electrical Engineering and Computer Science University of Michigan United States Dephy Inc. MaynardMA01754 United States

A key step in the development of lightweight, high performance robotic systems is the modeling and selection of permanent magnet brushless direct current (BLDC) electric motors. Typical modeling analyses are completed a priori, and provide insight for properly sizing a motor for an application, specifying the required operating voltage and current, as well as assessing the thermal response and other design attributes e.g. transmission ratio). However, to perform these modeling analyses, proper information about the motor’s characteristics are needed, which are often obtained from manufacturer datasheets. Through our own experience and communications with manufacturers, we have noticed a lack of clarity and standardization in modeling BLDC motors, compounded by vague or inconsistent terminology used in motor datasheets. The purpose of this tutorial is to concisely describe the governing equations for BLDC motor analyses used in the design process, as well as highlight potential errors that can arise from incorrect usage. We present a power-invariant conversion from phase and line-to-line reference frames to a familiar q-axis DC motor representation, which provides a "brushed" analogue of a three phase BLDC motor that is convenient for analysis and design. We highlight potential errors including incorrect calculations of winding resistive heat loss, improper estimation of motor torque via the motor’s torque constant, and incorrect estimation of the required bus voltage or resulting angular velocity limitations. A unified and condensed set of governing equations is available for designers in Appendix A. The intent of this work is to provide a consolidated mathematical foundation for modeling BLDC motors that addresses existing confusion and fosters high performance designs of future robotic systems. © 2023, CC BY-NC-ND.

关键词： Design

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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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Impedance control for a novel composite modular lower-limb hemiplegic exoskeleton

Impedance control for a novel composite modular lower-limb h...

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

作者： Bao, Ruoyu Li, Pengbo He, Bailin Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781665436786

This paper proposes a control scheme based on impedance control for the smoothing control of the operating movements of a lower-limb hemiplegic exoskeleton robot on the basis of a new composite modular lower-limb hemiplegic ex-oskeleton(CMLHE) structure. The CMLHE has good structural performance and can be used in the rehabilitation of patients with hemiplegia caused by stroke and other diseases. At the same time, the smoothing control by means of impedance control ensures that patients with lower limb hemiplegia can use the CMLHE to achieve smooth walking movements without additional injuries caused by the CMLHE during the rehabilitation training. This paper analyses the performance of this CMLHE robot based on walking movements and demonstrates through kinematic and kinetic modelling and simulation that the impedance control scheme of this CMLHE robot is feasible and effective and can be more widely used in the rehabilitation training of patients with lower limb hemiplegia. © 2021 IEEE.

关键词： Exoskeleton (robotics)

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

作者： Ma, Jiajun Liang, Guoyuan Liang, Yu Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

3D facial similarity measure is a critical research direction in fields such as 3D face recognition, craniofacial reconstruction, and heritability estimation, etc. and represents a challenging and meaningful task. In this paper, we propose a novel method based on deformation field to establish the similarity relationship between 3D faces. The main idea is divided into two steps as follows: (1) The landmarks are semi-automatically marked on all faces to build correspondences between landmarks (2) According to the distance field and the dense corresponding relationship of faces, a deformation field is generated between 3D faces that deform the source model to the target model. The path of deformation is computed to measure the similarity between two 3D faces. Comprehensive experiments conducted on the BU3DFE database illustrate that the similarity measure method is consistent with the subjective evaluation and provides high discriminability for different individuals. © 2021 IEEE.

关键词： Deformation

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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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