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IEEE International Conference on Robotics and automation (ICRA)

作者： Wendong Zheng Kun Liu Di Guo Wuqiang Yang Jun Zhu Huaping Liu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China Department of Computer Science and Technology Tsinghua University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Electronic Engineering The University of Manchester Manchester U.K. School of Automation Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Tactile sensing plays a critical role in enabling robots to interact safely with target objects in dynamic and unstructured environments. While various tactile sensors based on different sensing principles or different sensitive materials have been proposed, the development of flexible large-area tactile sensors for robots is still challenging. In this paper, a novel highly sensitive piezoresistive sponge based on multi-walled carbon nanotubes (MWCNTs) and polyurethane (PU) sponge is fabricated for pressure sensing. The sensing behavior of the piezoresistive sponge was experimentally evaluated, showing high sensitivity and fast response. Based on the piezoresistive sponge, a flexible large-area tactile sensor is designed for distributed force detection with electrical resistance tomography technology. The sensing performance of the sensor is validated by touch location, sensitivity analysis, real-time touch discrimination, and touch modality recognition. The experimental results indicate that the sensor performs well in detecting the position and force of contact in a large area. The sensor’s performance shows promise in embodied tactile sensing and human–robot interaction.

关键词： Fabrication Costs Sensitivity analysis Inverse problems Force Tactile sensors Reconstruction algorithms

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Generalized Category Discovery with Large Language Models in the Loop

arXiv

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arXiv 2023年

作者： An, Wenbin Shi, Wenkai Tian, Feng Lin, Haonan Wang, QianYing Wu, Yaqiang Cai, Mingxiang Wang, Luyan Chen, Yan Zhu, Haiping Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China Lenovo Research China University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) 1 into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://***/Lackel/LOOP. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Hierarchical Prompts for Rehearsal-free Continual Learning

arXiv

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arXiv 2024年

作者： Zuo, Yukun Yao, Hantao Yu, Lu Zhuang, Liansheng Xu, Changsheng The School of Information Science and Technology University of Science and Technology of China Hefei230026 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

Continual learning endeavors to equip the model with the capability to integrate current task knowledge while mitigating the forgetting of past task knowledge. Inspired by prompt tuning, prompt-based methods maintain a frozen backbone and train with slight learnable prompts to minimize the catastrophic forgetting that arises due to updating a large number of backbone parameters. Nonetheless, these learnable prompts tend to concentrate on the discriminatory knowledge of the current task while ignoring past task knowledge, leading to that learnable prompts still suffering from catastrophic forgetting. This paper introduces a novel rehearsal-free paradigm for continual learning termed Hierarchical Prompts (H-Prompts), comprising three categories of prompts - class prompt, task prompt, and general prompt. To effectively depict the knowledge of past classes, class prompt leverages Bayesian Distribution Alignment to model the distribution of classes in each task. To reduce the forgetting of past task knowledge, task prompt employs Cross-task Knowledge Excavation to amalgamate the knowledge encapsulated in the learned class prompts of past tasks and current task knowledge. Furthermore, general prompt utilizes Generalized Knowledge Exploration to deduce highly generalized knowledge in a self-supervised manner. Evaluations on two benchmarks substantiate the efficacy of the proposed H-Prompts, exemplified by an average accuracy of 87.8% in Split CIFAR-100 and 70.6% in Split ImageNet-R. Copyright © 2024, The Authors. All rights reserved.

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Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

arXiv

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arXiv 2024年

作者： Zhang, Tianyu Hou, Chengbin Jiang, Rui Zhang, Xuegong Zhou, Chenghu Tang, Ke Lv, Hairong The Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University Beijing100084 China The School of Computer Science and Engineering Fuyao Institute of Technology Fuzhou350109 China The State Key Laboratory of Resources and Environmental Information Systems Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Fuzhou Institute of Data Technology Fuzhou350200 China

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://***/zhangtia16/LICAP Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt engineering: Fundamental, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Liu, Yinqiu Du, Hongyang Niyato, Dusit Kang, Jiawen Cui, Shuguang Shen, Xuemin Zhang, Ping The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering University of Waterloo Canada The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

As the next-generation paradigm for content creation, AI-Generated Content (AIGC), i.e., generating content automatically by Generative AI (GAI) based on user prompts, has gained great attention and success recently. With the ever-increasing power of GAI, especially the emergence of Pretrained Foundation Models (PFMs) that contain billions of parameters and prompt engineering methods (i.e., finding the best prompts for the given task), the application range of AIGC is rapidly expanding, covering various forms of information for human, systems, and networks, such as network designs, channel coding, and optimization solutions. In this article, we present the concept of mobile-edge AI-Generated Everything (AIGX). Specifically, we first review the building blocks of AIGX, the evolution from AIGC to AIGX, as well as practical AIGX applications. Then, we present a unified mobile-edge AIGX framework, which employs edge devices to provide PFM-empowered AIGX services and optimizes such services via prompt engineering. More importantly, we demonstrate that suboptimal prompts lead to poor generation quality, which adversely affects user satisfaction, edge network performance, and resource utilization. Accordingly, we conduct a case study, showcasing how to train an effective prompt optimizer using ChatGPT and investigating how much improvement is possible with prompt engineering in terms of user experience, quality of generation, and network performance. © 2023, CC BY.

关键词： Network performance

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Spatio-Temporal Transformer-Based Reinforcement Learning for Robot Crowd Navigation

Spatio-Temporal Transformer-Based Reinforcement Learning for...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Haodong He Hao Fu Qiang Wang Shuai Zhou Wei Liu Yang Chen School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan China

Ensuring robots can move safely and adhere to social norms in dynamic human environments is a crucial step towards robot autonomous decision-making. In existing work, double serial separate modules are generally used to capture spatial and temporal interactions, respectively. However, such methods lead to extra difficulties in improving the utilization of spatio-temporal features and reducing the conservatism of navigation policy. In light of this, this paper proposes a spatiotemporal transformer-based policy optimization algorithm to more effectively preserve the human-robot interactions. Specifically, a gated embedding mechanism is introduced to effectively fuses the spatial and temporal representations by integrating both modalities at the feature level. Then Transformer is leveraged to encode the spatio-temporal semantic information, with the hope of finding the optimal navigation policy. Finally, a combination of spatio-temporal Transformer and self-adjusting policy entropy significantly reduce the conservatism of navigation policies. Experimental results demonstrate the priority of the proposed algorithm over the state-of-the-art methods.

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Scalable Federated Unlearning via Isolated and Coded Sharding

arXiv

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arXiv 2024年

作者： Lin, Yijing Gao, Zhipeng Du, Hongyang Niyato, Dusit Gui, Gui Cui, Shuguang Ren, Jinke State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Central South University China

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i.e., classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks. © 2024, CC BY.

关键词： Digital storage

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A Method of Vehicle Peripheral Objects Detection Based on Binocular Vision

A Method of Vehicle Peripheral Objects Detection Based on Bi...

引用

Chinese automation Congress (CAC)

作者： Xinyu Wang Hongyu Xia Zhichen Huang Zihao Yuan Wei Zhou Electricity and Automation Engineering College Xiamen University of Technology Xiamen China Department of Computer Science Central South University Changsha China Vehicle Testing Laboratory Xiamen Product Quality Supervision and Inspection Institute Xiamen China

Aiming at the collision problem that may occur when the passenger opens the door after the vehicle stops, this paper proposes an obj ect detection algorithm around the vehicle based on binocular vision. Due to the high computational complexity of binocular registration, the stereo correction of the image is realized by combining the dual-target fixed parameters and Bouguet Reprojection Algorithm, so that the binocular image is corrected into coplanar line alignment, and the operational efficiency is improved. The You Only Look Once algorithm is used to realize the recognition of people and vehicles on the road. In order to reduce the probability of missing detection and wrong detection of the algorithm in the remote scene, the up-sampling fusion method of the feature pyramid is adopted to detect the target on three feature layers, so as to realize the real-time detection and tracking of the target. Then, Scale-invariant Feature Transform Algorithm is used to obtain the matching points in the target area, and the distance between the feature points in the image and the camera is measured through coordinate transformation. At last, the system is tested at National Supervision &Inspection Centre for Inside Vehicle. The accuracy of the proposed algorithm for measuring the distance of various types of targets is above 96%.

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TOWARDS TRUSTWORTHY MULTI-LABEL SEWER DEFECT CLASSIFICATION VIA EVIDENTIAL DEEP LEARNING

arXiv

引用

arXiv 2022年

作者： Zhao, Chenyang Hu, Chuanfei Shao, Hang Wang, Zhe Wang, Yongxiong Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Sewers

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic Control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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