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20th International Workshop on Non-Monotonic Reasoning, NMR 2022

作者： Sridharan, Mohan Benz, Chloé Findelair, Arthur Gloaguen, Kévin Intelligent Robotics Lab School of Computer Science University of Birmingham United Kingdom Illinois Institute of Technology United States École Nationale Supérieure de Mécanique et d'Aérotechnique France

This paper describes the development of an architecture that combines non-monotonic logical reasoning and deep learning in virtual (simulated) and real (physical) environments for an assistive robot. As an illustrative example, we consider a robot assisting in a simulated restaurant environment. For any given goal, the architecture uses Answer Set Prolog to represent and reason with incomplete commonsense domain knowledge, providing a sequence of actions for the robot to execute. At the same time, reasoning directs the robot's learning of deep neural network models for human face and hand gestures made in the real world. These learned models are used to recognize and translate human gestures to scenarios that mimic real-world situations in the simulated environment, and to goals that need to be achieved by the robot in the simulated environment. We report the challenges faced in the development of such an integrated architecture, as well as the insights learned from the design, implementation, and evaluation of this architecture by a distributed team of researchers during the ongoing pandemic. © 2022 Copyright for this paper by its authors.

关键词： Robots

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Sudden Cardiac Arrest Detection Based on Temporal Features of ECG Using Support Vector Machine Classifier

Sudden Cardiac Arrest Detection Based on Temporal Features o...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Banerjee, Prakash Bhattacherjee, Saptak Dasgupta, Kousik Department of Electrical and Electronics Engineering University of Engineering and Management Kolkata India School of Mechatronics and Robotics Indian Institute of Engineering Science and Technology Howrah Shibpur India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani India

ISBN: (纸本)9789811670107

Sudden cardiac arrest (SCA) is caused due to malfunctioning in heart rhythm, and the patient needs to be hospitalized as early as possible just after the onset of SCA;otherwise, it can lead to the death of the patient. Therefore, detection of SCA with a very high accuracy and within a very short time after the onset of symptoms is very crucial for patient survival. Several approaches for detection of SCA have been made using temporal and morphological variations in the electrocardiogram (ECG). However, the existing methods suffer from low accuracy or involve long-term monitoring of ECG. Therefore, in the present work, an intelligent algorithm to detect SCA based on the extraction of temporal features like RR interval and the QT interval from ECG and subsequently classifying them using support vector machine (SVM) has been proposed. Experimental evaluation of the proposed method on five SCA and five normal subjects demonstrates a high classification accuracy of 100% which proves the superiority of the proposed algorithm as an effective method for detection of SCA. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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3D occluded object visualization by using integral imaging and semantic segmentation 13

3D occluded object visualization by using integral imaging a...

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13th International Conference on Information and Communication technology Convergence, ICTC 2022

作者： Honda, Kazuaki Lee, Jaehoon Kim, Hyun-Woo Uchino, Hideaki Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Dept. of Computer Science and Networks Fukuoka Iizuka Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Anseong Kyonggi-do Anseong Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this paper, we propose the improved occluded object visualization method by using integral imaging and semantic segmentation. Integral imaging is the passive 3D visualization technique that can generate 3D information through elemental images that have different perspectives of information on 3D objects. Moreover, it can be used to remove the occlusion in the 3D scene. The elemental image's various perspective information can be utilized to remove the occlusion in the 3D scene via the 3D image reconstruction process. However, the occlusion object pixels in the elemental image can degrade the image quality of the 3D image. Therefore, it is difficult to visualize the object without occlusion, clearly. To solve this problem, we propose the occluded object visualization method that can remove the occlusion and can visualize the target 3D object by using semantic segmentation. Semantic segmentation is the machine learning technique that can recognize the labeled object in the scene. Therefore, it can generate the specific labeled object mask image. Then, our proposed method can generate accurate 3D target object information. To prove our method, we carry out the simulation experiment and evaluate image quality with a correlation metric. © 2022 IEEE.

关键词： Image quality

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DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving

arXiv

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arXiv 2024年

作者： Guo, Xianda Zhang, Ruijun Duan, Yiqun He, Yuhang Zhang, Chenming Liu, Shuai Chen, Long School of Computer Science Wuhan University China Institute of Automation Chinese Academy of Sciences China Waytous China HAI Centre AAII School of Computer Science University of Technology Sydney Australia Department of Computer Science University of Oxford United Kingdom TikTok China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Autonomous driving requires a comprehensive understanding of 3D environments to facilitate high-level tasks such as motion prediction, planning, and mapping. In this paper, we introduce DriveMLLM, a benchmark specifically designed to evaluate the spatial understanding capabilities of multimodal large language models (MLLMs) in autonomous driving. DriveMLLM includes 880 front-facing camera images and introduces both absolute and relative spatial reasoning tasks, accompanied by linguistically diverse natural language questions. To measure MLLMs’ performance, we propose novel evaluation metrics focusing on spatial understanding. We evaluate several state-of-the-art MLLMs on DriveMLLM, and our results reveal the limitations of current models in understanding complex spatial relationships in driving contexts. We believe these findings underscore the need for more advanced MLLM-based spatial reasoning methods and highlight the potential for DriveMLLM to drive further research in autonomous driving. Code will be available at https://***/XiandaGuo/Drive-MLLM. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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An improved 3D human model reconstruction technique based on Cascade MVSNet

An improved 3D human model reconstruction technique based on...

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2021 International Conference on Signal Image Processing and Communication, ICSIPC 2021

作者： Huang, Qiubo Zhou, Chao Zhu, Zhenwei Lu, Ting School of Computer Science and Technology Donghua University Shanghai China Shanghai Enfon Robotics Co. Ltd Shanghai China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

In order to improve the capability of feature point extraction and matching in 3D human model reconstruction and enhance the accuracy of it, this paper improves a multi-view-based 3D reconstruction method of the human body model. We first take dozens of photos of a person at the angle of a circle around him, and then perform instance segmentation on the images to remove the background. Next, we introduce Laplace operator in extracting image feature points to sharpen the edges of the images and use KNN algorithm to match feature points on the images. When filtering the matching points, we introduce the Adaptive Locally-Affine Matching (AdaLAM) algorithm to optimize the matching results. Finally, we use incremental SFM and Cascade MVSNet to reconstruct sparse and dense point clouds, respectively, and obtain better reconstruction results. The results show that, compared with the original 3D reconstruction algorithm, the improved method in this paper effectively improves the capability of feature point extraction and matching, enhances the accuracy of the generated human dense point clouds. As a result, the number of point clouds obtained increased by 17.8%. © 2021 SPIE.

关键词： Three dimensional computer graphics

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Semantic-guided Image Feature Matching for Agricultural Robots 21

Semantic-guided Image Feature Matching for Agricultural Robo...

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Proceedings of the 2021 4th International Conference on Artificial Intelligence and Pattern Recognition

作者： Zhenghong Yu Xiuli Zhu Huabing Zhou College of Robotics Guangdong Polytechnic of Science and Technology China School of Computer Science and Engineering Wuhan Institute of Technology China

ISBN: (纸本)9781450384087

Image feature matching is one of the most important basic techniques for agricultural robots. This task has been tried, but because of the complexity of the image, especially the non-rigid transformations, the performance is still far from satisfactory. This paper proposed a semantic-guided feature matching method for both agricultural mobile robot and UAV via semantic segmentation and probabilistic inference. Given an original image pair, a learning-based method, which is very active to the challenging variation of outdoor luminosity, is introduced firstly to produce a sematic image pair. This step shifts the robot's focus to the task itself, while improving matching efficiency at the same time. Then, based on the characteristic similarity of the ROI region of the semantic image pair, a series of putative correspondences are generated. Next, the focuses on eliminating outliers from that series while deducting the transformation, where this procedure was developed on a maximum likelihood Bayesian model. The paper has also applied three effective regularization techniques to the correspondence, which helped to come up with the best solution. Finally, as the problem was solved by EM algorithm and extensive experiments on those images taken from the real farmland, it is safe to say that the accurate results of this method are superior to other state-of-the-art methods.

关键词： Agricultural mobile robot Image feature matching Probabilistic inference Semantic segmentation Unmanned aerial vehicle

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MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation

arXiv

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arXiv 2025年

作者： Zhang, Lingfeng Hao, Xiaoshuai Xu, Qinwen Zhang, Qiang Zhang, Xinyao Wang, Pengwei Zhang, Jing Wang, Zhongyuan Zhang, Shanghang Xu, Renjing The Hong Kong University of Science and Technology Guangzhou China Beijing Academy of Artificial Intelligence China Beijing Innovation Center of Humanoid Robotics Co. Ltd China School of Computer Science Wuhan University China State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China

Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring agents to navigate diverse and unseen environments while following natural language instructions. Traditional approaches rely heavily on historical observations as spatio-temporal contexts for decision making, leading to significant storage and computational overhead. In this paper, we introduce MapNav, a novel end-to-end VLN model that leverages Annotated Semantic Map (ASM) to replace historical frames. Specifically, our approach constructs a top-down semantic map at the start of each episode and update it at each timestep, allowing for precise object mapping and structured navigation information. Then, we enhance this map with explicit textual labels for key regions, transforming abstract semantics into clear navigation cues and generate our ASM. MapNav agent using the constructed ASM as input, and use the powerful end-to-end capabilities of VLM to empower VLN. Extensive experiments demonstrate that MapNav achieves state-of-the-art (SOTA) performance in both simulated and real-world environments, validating the effectiveness of our method. Moreover, we will release our ASM generation source code and dataset to ensure reproducibility, contributing valuable resources to the field. We believe that our proposed MapNav can be used as a new memory representation method in VLN, paving the way for future research in this field. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection

arXiv

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arXiv 2023年

作者： Chapman, Nicolas Harvey Dayoub, Feras Browne, Will Lehnert, Christopher The School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia The School of Computer Science The Australian Institute of Machine Learning The University of Adelaide Adelaide Australia

Unsupervised Domain Adaptive Object Detection (UDA-OD) uses unlabelled data to improve the reliability of robotic vision systems in open-world environments. Previous approaches to UDA-OD based on self-training have been effective in overcoming changes in the general appearance of images. However, shifts in a robot’s deployment environment can also impact the likelihood that different objects will occur, termed class distribution shift. Motivated by this, we propose a framework for explicitly addressing class distribution shift to improve pseudo-label reliability in self-training. Our approach uses the domain invariance and contextual understanding of a pre-trained joint vision and language model to predict the class distribution of unlabelled data. By aligning the class distribution of pseudo-labels with this prediction, we provide weak supervision of pseudo-label accuracy. To further account for low quality pseudo-labels early in self-training, we propose an approach to dynamically adjust the number of pseudo-labels per image based on model confidence. Our method outperforms state-of-the-art approaches on several benchmarks, including a 4.7 mAP improvement when facing challenging class distribution shift. Code available at https://***/nhcha6/ClassDistributionPrediction Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Comparison of Rating Scale and Pairwise Comparison Methods for Measuring Human Co-worker Subjective Impression of Robot during Physical Human-Robot Collaboration

Comparison of Rating Scale and Pairwise Comparison Methods f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiao Wang Ziqi Wang Marc G. Carmichael Dikai Liu Chin-Teng Lin Robotics Institute Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity *** variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

关键词： Surveys Accuracy NASA Collaboration Reliability engineering Particle measurements Task analysis

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Design of Low-Cost Approximate CMOS Full Adders

Design of Low-Cost Approximate CMOS Full Adders

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Aibin Yan Shaojie Wei Zhixing Li Jie Cui Zhengfeng Huang Patrick Girard Xiaoqing Wen School of Computer Science and Technology Anhui University Hefei China School of Microelectronics Hefei University of Technology Hefei China Laboratory of Informatics Robotics and Microelectronics of Montpellier University of Montpellier / CNRS Montpellier France Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

Many applications have an inherent tolerance for insignificant inaccuracies. Full adders are key arithmetic functions for many error-tolerant applications. Approximate full adders are considered an efficient technique to trade off energy relative to performance and accuracy. In this paper, we propose four approximate full adders with low overhead. The proposed and the existing approximate full adders are classified into two groups according to their error distances. Simulation results show that, compared with the existing approximate full adders, in the first group, the proposed ones can reduce power-area-delay product (PADP) by 61.83%, power by 54.15%, area by 44.67%, and delay by 22.78 % on average; in the second group, the proposed ones can reduce PADP by 97.01%, power by 93.43%, area by 24.98%, and delay by 36.14% on average.

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