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arXiv 2024年

作者： Song, Ziying Jia, Feiyang Pan, Hongyu Luo, Yadan Jia, Caiyan Zhang, Guoxin Liu, Lin Ji, Yang Yang, Lei Wang, Li School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Lab of Traffic Data Analysis and Mining China Horizon Robotics The University of Queensland Australia Hebei University of Science and Technology China Tsinghua University China Beijing Institute of Technology China

In the field of 3D object detection tasks, fusing heterogeneous features from LiDAR and camera sensors into a unified Bird's Eye View (BEV) representation is a widely adopted paradigm. However, existing methods are often compromised by imprecise sensor calibration, resulting in feature misalignment in LiDAR-camera BEV fusion. Moreover, such inaccuracies result in errors in depth estimation for the camera branch, ultimately causing misalignment between LiDAR and camera BEV features. In this work, we propose a novel ContrastAlign approach that utilizes contrastive learning to enhance the alignment of heterogeneous modalities, thereby improving the robustness of the fusion process. Specifically, our approach includes the L-Instance module, which directly outputs LiDAR instance features within LiDAR BEV features. Then, we introduce the C-Instance module, which predicts camera instance features through RoI (Region of Interest) pooling on the camera BEV features. We propose the InstanceFusion module, which utilizes contrastive learning to generate similar instance features across heterogeneous modalities. We then use graph matching to calculate the similarity between the neighboring camera instance features and the similarity instance features to complete the alignment of instance features. Our method achieves state-of-the-art performance, with an mAP of 70.3%, surpassing BEVFusion by 1.8% on the nuScenes validation set. Importantly, our method outperforms BEVFusion by 7.3% under conditions with misalignment noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization

arXiv

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arXiv 2024年

作者： Omisore, Olatunji Mumini Akinyemi, Toluwanimi Nguyen, Anh Wang, Lei Research Center for Medical Robotics and Minimally Invasive Surgical Devices Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Smart Robotic Lab Department of Computer Science University of Liverpool United Kingdom

Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking of endovascular tools during cardiac procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is both labor-intensive time consuming, and costly. In this study, we propose a weakly-supervised learning method with multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations, augmenting available partial labels. The pseudo labels are self-generated using a mixed loss function with shared consistency across the decoders. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the model could perform closer to fully-supervised models. Also, the proposed weakly-supervised multi-lateral method outperforms three well known methods used for weakly-supervised learning, offering the highest segmentation performance across the three angiogram datasets. Furthermore, numerous ablation studies confirmed the model’s consistent performance under different parameters. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean processing time of 35.26±11.29 ms per frame. This study provides a fast, stable and less expensive method for real-time tool segmentation and visua

关键词： Self-supervised learning

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Transformers Unveiled: A Comprehensive Evaluation of Emotion Detection in Text Transcription

Transformers Unveiled: A Comprehensive Evaluation of Emotion...

引用

Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Sara Ali Bushra Naz Sanam Narejo Sahil Ali Jitander Kumar Pabani National Center of Robotics and Automation – CMS Lab Mehran University of Engineering and Technology Jamshoro Pakistan Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Pakistan National Center of Rorotics and Automation - CMS Lab Mehran University of Engineering and Technology Jamshoro Pakistan Department of Telecommunication Engineering Dawood University of Engineering and Technology Karachi Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

The fast growth of digital communication via smartphones and social media has made textual data as one of the most essential forms of people's expressions. Accurate emotion discovery from text is thus important for applications in customer sentiment analysis and interactive technologies, be it a web, desktop, or mobile application, an intelligent robot, or minor connected devices. This paper is intended to evaluate the performance of five state-of-the-art transformer models, including BERT, ALBERT, DistilBERT, Transformer XL, and RoBERTa, for multiclass emotion detection using the IEMOCAP dataset. From the results, it is observed that DistilBERT has the highest accuracy of 79.2%, implying an effective approach in balancing model size and performance. These research show that smaller models like DistilBERT often outperform larger models by reducing their overfitting and computational demands. The findings also highlights the need for task-specific fine-tuning and optimization in emotion detection.

关键词： Wireless communication Emotion recognition Adaptation models Visualization Accuracy Social networking (online) Computational modeling Transfer learning Transformers Context modeling

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Hi-EF: Benchmarking Emotion Forecasting in Human-interaction

arXiv

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arXiv 2024年

作者： Wang, Haoran Mai, Xinji Tao, Zeng Wang, Yan Yu, Jiawen Zhou, Ziheng Tong, Xuan Yan, Shaoqi Zhao, Qing Gao, Shuyong Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China

Affective Forecasting, a research direction in psychology that predicts individuals' future emotions, is often constrained by numerous external factors like social influence and temporal distance. To address this, we transform Affective Forecasting into a Deep Learning problem by designing an Emotion Forecasting paradigm based on two-party interactions. We propose a novel Emotion Forecasting (EF) task grounded in the theory that an individual's emotions are easily influenced by the emotions or other information conveyed during interactions with another person. To tackle this task, we have developed a specialized dataset, Humaninteraction- based Emotion Forecasting (Hi-EF), which contains 3069 two-party Multilayered-Contextual Interaction Samples (MCIS) with abundant affectiverelevant labels and three modalities. Hi-EF not only demonstrates the feasibility of the EF task but also highlights its potential. Additionally, we propose a methodology that establishes a foundational and referential baseline model for the EF task and extensive experiments are provided. The dataset and code is available HERE. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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A Stable Guidance Method for Teleoperation-based Robot Learning from Demonstration

A Stable Guidance Method for Teleoperation-based Robot Learn...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Weiyong Si Zhehao Jin Zhenyu Lu Ning Wang Chenguang Yang School of Computer Science and Electronic Engineering at the University of Essex Colchester UK College of Information Engineering Zhejiang University of Technology Hangzhou China Bristol Robotics Lab at the University of the West of England Bristol UK Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Robot learning from teleoperation-based demonstration has been proven to be an effective method for robot skills learning. However, the human demonstration process is mentally and physically challenging due to the dexterous and continuous interaction control. Especially, for contact-rich tasks human operators need to control the pose and contact force between the end-effector and the environment, only based on visual and haptic feedback. To tackle the problem, this work studies a stable guidance method for teleoperation-based demonstration to improve the experience and performance of human demonstration and reduce the workload of human operators. The guidance design is based on a dynamic system-based imitation learning model, which is achieved by human demonstration. The virtual guidance force will decrease as the virtual guidance’s iterative learning. The efficiency of human demonstration is improved, and human operators’ physical and mental workload is reduced. Two typical experiments, robot-assisted polishing and robot-assisted ultrasound scanning, are conducted to validate the performance of the guidance method.

关键词： Visualization Ultrasonic imaging Imitation learning Force Dynamics Process control Robot learning End effectors Haptic interfaces Iterative methods

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Improving Adversarial Transferability with Neighbourhood Gradient Information

arXiv

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arXiv 2024年

作者： Guo, Haijing Wang, Jiafeng Chen, Zhaoyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Li, Jinglun Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai200433 China

Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack scenarios, a considerable attack performance gap between the surrogate model and the target model persists. This work focuses on enhancing the transferability of adversarial examples to narrow this performance gap. We observe that the gradient information around the clean image, i.e. Neighbourhood Gradient Information, can offer high transferability. Leveraging this, we propose the NGI-Attack, which incorporates Example Backtracking and Multiplex Mask strategies, to use this gradient information and enhance transferability fully. Specifically, we first adopt Example Backtracking to accumulate Neighbourhood Gradient Information as the initial momentum term. Multiplex Mask, which forms a multi-way attack strategy, aims to force the network to focus on non-discriminative regions, which can obtain richer gradient information during only a few iterations. Extensive experiments demonstrate that our approach significantly enhances the adversarial transferability. Especially, when attacking numerous defense models, we achieve an average attack success rate of 95.8%. Notably, our method can plugin with any off-the-shelf algorithm to improve their attack performance without additional time cost. Copyright © 2024, The Authors. All rights reserved.

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Design and Modelling of Electrical Impedance Tomography-based 3D-Printed Patterned Soft Tactile skins

Design and Modelling of Electrical Impedance Tomography-base...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Yunqi Huang David Hardman Christopher Bascucci Frank Clemens Thomas George Thuruthel Department of Computer Science University College London London UK Bio-Inspired Robotics Lab University of Cambridge Cambridge UK Department of Functional Materials Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

The ability to estimate contact information is becoming increasingly relevant for robotic applications. Consequently, there is a growing need for technologies that can provide dense and distributed contact information in a compact form factor. This work presents the design and modeling of a thin-form-factor tactile skin based on electrical impedance tomography measurements. This technology enables the creation of tactile skins of arbitrary size without requiring any embedded electronic components. We demonstrate how patterning these 3D-printed skins can improve the accuracy and precision of contact localization and force estimation. A comprehensive framework for the automatic fabrication and calibration of tactile skins is presented, paving the way for customizable soft tactile skins.

关键词： Location awareness Fabrication Impedance measurement Contacts Force Estimation Electronic components Soft robotics Skin Impedance

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DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

arXiv

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arXiv 2023年

作者： Zhang, Zheng Zhang, Dengyu Zhang, Qingrui Pan, Wei Hu, Tianjiang Lab. Sun Yat-Sen University Shenzhen China Department of Computer Science The University of Manchester United Kingdom Department of Cognitive Robotics Delft University of Technology Netherlands

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This paper proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analysis and ablation studies are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of deploying DACOOP-A in physical systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning

arXiv

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arXiv 2024年

作者： Li, Jinglun Zhou, Xinyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Chen, Zhaoyu Ge, Weifeng Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing methods for out-of-distribution (OOD) detection, a critical area where AI models encounter unseen data in real-world scenarios, rely heavily on whole-image features. These image-level features can include irrelevant information that hinders the detection of OOD samples, ultimately limiting overall performance. In this paper, we propose TagOOD, a novel approach for OOD detection that leverages visionlanguage representations to achieve label-free object feature decoupling from whole images. This decomposition enables a more focused analysis of object semantics, enhancing OOD detection performance. Subsequently, TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection. Finally, our approach efficiently detects OOD samples by calculating distance-based metrics as OOD scores between learned centers and test samples. We conduct extensive experiments to evaluate TagOOD on several benchmark datasets and demonstrate its superior performance compared to existing OOD detection methods. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, with potential applications across various tasks. Code is available at: https://***/Jarvisgivemeasuit/tagood. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Convolutional Cross-View Pose Estimation

arXiv

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arXiv 2023年

作者： Xia, Zimin Booij, Olaf Kooij, Julian F.P. The Intelligent Vehicles Group Department of Cognitive Robotics Delft University of Technology Leeghwaterstraat Delft2628 CN Netherlands The Computer Vision Lab Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

We propose a novel end-to-end method for cross-view pose estimation. Given a ground-level query image and an aerial image that covers the query’s local neighborhood, the 3 Degrees-of-Freedom camera pose of the query is estimated by matching its image descriptor to descriptors of local regions within the aerial image. The orientation-aware descriptors are obtained by using a translationally equivariant convolutional ground image encoder and contrastive learning. The Localization Decoder produces a dense probability distribution in a coarse-to-fine manner with a novel Localization Matching Upsampling module. A smaller Orientation Decoder produces a vector field to condition the orientation estimate on the localization. Our method is validated on the VIGOR and KITTI datasets, where it surpasses the state-of-the-art baseline by 72% and 36% in median localization error for comparable orientation estimation accuracy. The predicted probability distribution can represent localization ambiguity, and enables rejecting possible erroneous predictions. Without re-training, the model can infer on ground images with different field of views and utilize orientation priors if available. On the Oxford RobotCar dataset, our method can reliably estimate the ego-vehicle’s pose over time, achieving a median localization error under 1 meter and a median orientation error of around 1 degree at 14 FPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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