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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Zhegong Shangguan Mengyuan Ding Chuang Yu Chaona Chen Adriana Tapus Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris 828 Boulevard des Maréchaux Palaiseau France National Engineering Laboratory for Visual Information Applications college of Artificial Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Cognitive Robotics Lab Department of Computer Science University of Manchester Kilburn Building Oxford Rd Manchester UK School of Psychology and Neuroscience University of Glasgow Glasgow UK

To develop robots that can show cognitive functions, we must learn from the knowledge of human cognition. Existing biological and psychological evidence suggests that self-face perception and sensorimotor learning mechanisms play a crucial role in self-recognition. However, one of the most important self-identity cues – facial information – has not been extensively studied in the robot self-recognition task. Current research on robot self-recognition primarily relies on the recognition of high-precision targets and tracking of manipulator motions, where the self-perception of facial information is not well studied. In this work, we propose a novel approach to achieve self-recognition via self-perception of facial expressions. Specifically, we developed a Conditional Generative Adversarial Network (CGAN) model using the knowledge on human cognitive and sensorimotor functions. It allows the robot to be aware of self-face (i.e., off-line model). Passing the observed visual variations in a mirror and comparing them to self-perceptive information, the robot can recognize the self through an online Bayesian learning regression. The results of our first experiment show that the robot can recognize itself in a mirror. The results from the second experiment show that our algorithm could be tricked by a similar robot with the same facial expressions, which is similar to the rubber hand illusion (RHI).

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ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

arXiv

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arXiv 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Laboratory of Signals systems Artificial Intelligence and networks Sfax Tunisia Multimedia Information systems and Advanced Computing Laboratory Tunisia Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluation on three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, CC BY.

关键词： Image enhancement

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PR2: A Physics- and Photo-realistic Humanoid Testbed with Pilot Study in Competition

arXiv

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arXiv 2024年

作者： Liu, Hangxin Xie, Qi Zhang, Zeyu Yuan, Tao Leng, Xiaokun Sun, Lining Zhu, Song-Chun Zhang, Jingwen He, Zhicheng Su, Yao Beijing100080 China Institute for Artificial Intelligence School of Artificial Intelligence Peking University Beijing100871 China Department of Automation Tsinghua University Beijing100084 China Department of Computer Science Harbin Institute of Technology Harbin150001 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215000 China

This paper presents the development of a Physicsrealistic and Photo-realistic humanoid robot testbed, PR2, to facilitate collaborative research between Embodied artificial intelligence (Embodied AI) and robotics. PR2 offers high-quality scene rendering and robot dynamic simulation, enabling (i) the creation of diverse scenes using various digital assets, (ii) the integration of advanced perception or foundation models, and (iii) the implementation of planning and control algorithms for dynamic humanoid robot behaviors based on environmental feedback. The beta version of PR2 has been deployed for the simulation track of a nationwide full-size humanoid robot competition for college students, attracting 137 teams and over 400 participants within four months. This competition covered traditional tasks in bipedal walking, as well as novel challenges in loco-manipulation and language-instruction-based object search, marking a first for public college robotics competitions. A retrospective analysis of the competition suggests that future events should emphasize the integration of locomotion with manipulation and perception. By making the PR2 testbed publicly available at https://***/pr2-humanoid/PR2-Platform, we aim to further advance education and training in humanoid robotics. © 2024, CC BY.

关键词： Anthropomorphic robots

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St-Keys: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

SSRN

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SSRN 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia SM@RTS: Laboratory of Signals systeMs aRtificial Intelligence and neTworkS MIR@CL: Multimedia InfoRmation systems and Advanced Computing Laboratory Computer Vision Center Computer Science Department Universitat Aut`onoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluationon three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Improving AlphaFLOW for Efficient Protein Ensembles Generation

arXiv

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arXiv 2024年

作者： Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Shanghai Institute of Materia Medica Chinese Academy of Sciences China

Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFLOW stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFLOW still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFLOW-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFLOW-Lit performs on-par with AlphaFLOW and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFLOW-Lit in enabling faster and more scalable generation of protein ensembles. © 2024, CC BY.

关键词： Proteins

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Spiral coverage path planning for Multi-UAV photovoltaic panel inspection applications

Spiral coverage path planning for Multi-UAV photovoltaic pan...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marco Andrés Luna Mohammad Sadeq Ale Isaac Miguel Fernandez-Cortizas Carlos Santos Ahmed Refaat Ragab Martin Molina Pascual Campoy Computer Vision and Aerial Robotics Group (CVAR) Centre for Automation and Robotics (C.A.R.) Universidad Politécnica de Madrid (UPM-CSIC) Madrid Spain Department of Signal Theory and Communications University of Alcala (UAH) Madrid Spain Department of Network Faculty of Information Systems and Computer Science October 6 University Giza Egypt Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid Spain

This paper deals with the problem of coverage path planning for multiple UAVs in disjoint regions. For this purpose, a spiral-coverage path planning algorithm is proposed. Additionally, task assignment methods for multi-region inspection with a swarm of UAVs are applied. The centralized system architecture is described, and an adaptive sliding mode controller is designed. Furthermore, we evaluate the performance of the proposed techniques by obtaining numerical results and simulations with the controller. The results show that the spiral pattern optimizes the cost of the mission and improves the task distribution of the mission planning system. Additionally, the performance of the proposed controller is robust to simulated disturbances.

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Modelling Handed Shearing Auxetics: Selective Piecewise Constant Strain Kinematics and Dynamic Simulation

Modelling Handed Shearing Auxetics: Selective Piecewise Cons...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Maximilian Stölzle Lillian Chin Ryan L. Truby Daniela Rus Cosimo Della Santina Cognitive Robotics department Delft University of Technology Delft CD Netherlands MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA Departments of Materials Science & Engineering and Mechanical Engineering Northwestern University Evanston IL USA Institute of Robotics and Mechatronics German Aerospace Center (DLR) Weßling Germany

Electrically-actuated continuum soft robots based on Handed Shearing Auxetics (HSAs) promise rapid actuation capabilities while preserving structural compliance. However, the foundational models of these novel actuators required for precise control strategies are missing. This paper proposes two key components extending discrete Cosserat rod model (DCM) to allow for modeling HSAs. First, we propose a mechanism for incorporating the auxetic trajectory into DCM dynamical simulations. We also propose an implementation of this extension as a plugin for the Elastica simulator. Second, we introduce a Selective Piecewise Constant Strain (SPCS) kinematic parameterization that can describe an HSA segment's shape with fewer configuration variables. We verify both theoretical contributions experimentally. The simulator is used to replicate experimental data of the mechanical characterization of HSA rods. For the second component, we attach motion capture markers at various points to a parallel HSA robot and find that the shape of the HSAs can be kinematically represented with an average accuracy of 0.3 mm for positions and 0.07 rad for orientations.

关键词： Auxetic materials Shape Motion segmentation Dynamics Kinematics Soft robotics Motion capture

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Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately

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Research 2022年第4期2022卷 137-146页

作者： Xiong Ying Si-Yang Leng Huan-Fei MaD Qing Nie Ying-Cheng Lai Wei Lin School of Mathematical Sciences SCMSand SCAMFudan UniversityShanghai 200433China Research Institute for Inteligent Complex Systems CCSBand LCNBIFudan UniversityShanghai 200433China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain ScienceFudan UniversityShanghai 200032China Institute of AI and Robotics Academy for Engineering and TechnologyFudan UniversityShanghai 200433China School of Mathematical Sciences Soochow UniversitySuzhou 215006China Department of Mathematics Department of Developmental and Cll BiologyAnd NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of CaliforniaIrvineCA 92697-3875USA School of Electrical ComputerAnd Energy EngineeringArizona State UniversityTempeArizona 85287-5706USA Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Data-based detection and quantification of causation in complex,nonlinear dynamical systems is of paramount importance to science,engineering,and *** by the widely used methodology in recent years,the cross-map-based techniques,we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms,which is consistent with the natural interpretation of *** particular,instead of measuring the smoothness of the cross-map as conventionally implemented,we define causation through measuring the scaling law for the continuity of the investigated dynamical system *** uncovered scaling law enables accurate,reliable,and eficient detection of causation and assessment of its strength in general complex dynamical systems,outperforming those existing representative *** continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world.

关键词： dynamical scaling continuity

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Conceptor Learning for Class Activation Mapping Based Visual Explanations

SSRN

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SSRN 2023年

作者： Zhong, Ke Yang, Zhen-Qun Zhang, Xu-Lu Zhang, Lei Qian, Guangwu Wei, Xiao-Yong Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610065 China Department of Computing The Hong Kong Polytechnic University Hong Kong Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong Sichuan University-Pittsburgh Institute Sichuan University Chengdu610065 China College of Computer Science Sichuan University Chengdu610065 China

Class Activation Mapping (CAM) has been widely adopted to generate saliency maps which provides visual explanations for deep neural networks (DNNs). The saliency maps are conventionally generated by fusing the channels of the target feature map using a weighted average scheme. It is a weak model for the inter-channel relation, in the sense that it only models the relation among channels in a contrastive way (i.e., channels that play key roles in the prediction are given higher weights for them to stand out in the fusion). The collaborative relation, which makes the channels work together to provide cross reference, has been ignored. Furthermore, the model has neglected the intra-channel relation thoroughly. In this paper, we address this problem by introducing Conceptor learning into CAM generation. Conceptor leaning has been originally proposed to model the patterns of state changes in recurrent neural networks (RNNs). By relaxing the dependency of Conceptor learning to RNNs, we make Conecptor Class Activation Mapping (Conceptor-CAM) not only generalizable to more DNN architectures but also able to learn both the inter- and intra-channel relations for better saliency map generation. Moreover, we have enabled the use of Boolean operations to combine the positive and pseudo-negative evidences, which has made the CAM inference more robust and comprehensive. The effectiveness of Conceptor-CAM has been validated with both formal verifications and experiments on the dataset of the largest scale in literature. The experimental results show that Conceptor-CAM is compatible with and can bring significant improvement to all well recognized CAM-based methods. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation

arXiv

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arXiv 2024年

作者： Liu, Kang Ma, Zhuoqi Liu, Mengmeng Jiao, Zhicheng Kang, Xiaolu Miao, Qiguang Xie, Kun The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Xi'an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China The Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Artificial Intelligence Xidian University Xi’an710071 China The Department of Diagnostic Imaging Brown University ProvidenceRI02903-4923 United States

A radiology report comprises presentation-style vocabulary, which ensures clarity and organization, and factual vocabulary, which provides accurate and objective descriptions based on observable findings. While manually writing these reports is time-consuming and labor-intensive, automatic report generation offers a promising alternative. A critical step in this process is to align radiographs with their corresponding reports. However, existing methods often rely on complete reports for alignment, overlooking the impact of presentation-style vocabulary. To address this issue, we propose FSE, a two-stage Factual Serialization Enhancement method. In Stage 1, we introduce factuality-guided contrastive learning for visual representation by maximizing the semantic correspondence between radiographs and corresponding factual descriptions. In Stage 2, we present evidence-driven report generation that enhances diagnostic accuracy by integrating insights from similar historical cases structured as factual serialization. Experiments on MIMIC-CXR and IU X-ray datasets across specific and general scenarios demonstrate that FSE outperforms state-of-the-art approaches in both natural language generation and clinical efficacy metrics. Ablation studies further emphasize the positive effects of factual serialization in Stage 1 and Stage 2. The code is available at https://***/mk-runner/FSE. Copyright © 2024, The Authors. All rights reserved.

关键词： Report generators

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