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Robust tube-based MPC with smooth computation for dexterous robot manipulation

science China(information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Improving Smart Contract Security with Contrastive Learning-Based Vulnerability Detection 24

Improving Smart Contract Security with Contrastive Learning-...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Chen, Yizhou Sun, Zeyu Gong, Zhihao Hao, Dan Moe Beijing China Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory Beijing China

ISBN: (纸本)9798400702174

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep learning to mitigate this threat. They treat each input contract as an independent entity and feed it into a deep learning model to learn vulnerability patterns by fitting vulnerability labels. It is a pity that they disregard the correlation between contracts, failing to consider the commonalities between contracts of the same type and the differences among contracts of different types. As a result, the performance of these methods falls short of the desired level. To tackle this problem, we propose a novel Contrastive Learning Enhanced Automated Recognition Approach for Smart Contract Vulnerabilities, named Clear. In particular, Clear employs a con-trastive learning (CL) model to capture the fine-grained correlation information among contracts and generates correlation labels based on the relationships between contracts to guide the training pro-cess of the CL model. Finally, it combines the correlation and the semantic information of the contract to detect SCVs. Through an empirical evaluation of a large-scale real-world dataset of over 40K smart contracts and compare 13 state-of-the-art baseline methods. We show that Clear achieves (1) optimal performance over all base-line methods;(2) 9.73%-39.99% higher F1-score than existing deep learning methods. © 2024 ACM.

关键词： Smart contract

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In-memory computing based on phase change memory for high energy efficiency

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science China(information sciences) 2023年第10期66卷 20-41页

作者： Luchang HE Xi LI Chenchen XIE Zhitang SONG National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Micro-System and Information Technology Chinese Academy of Sciences School of Microelectronics University of Science and Technology of China

The energy efficiency issue caused by the memory wall in traditional von Neumann architecture is difficult to reconcile. In-memory computing(CIM) based on emerging nonvolatile memory(NVM) is a promising solution to avoid data movement between storage and processors and realize highly energy-efficient computing. Compared with other NVM technologies, phase change random access memory(PCM) exhibits comprehensive performance for analog computing. In this paper, we review advanced PCM techniques,including phase-change materials, mechanisms, and unique properties, as a foundation and inspiration for implementing CIM architecture. Meanwhile, state-of-the-art PCM-based CIM systems are well discussed for high energy efficiency in artificial neural networks, spiking neural networks, and other artificial intelligence(AI) applications. Finally, we present the remaining challenges and potential solutions of CIM for further investigation.

关键词： in-memory computing phase change memory energy efficiency neural network memory wall

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Lightweight Curve Estimation Method for Low-Light Image Enhancement 7

Lightweight Curve Estimation Method for Low-Light Image Enha...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Yin, Guorong Wang, Lingfeng Li, Haichang Li, Yilun College Of Information Science And Technology Beijing University Of Chemical Technology Beijing100029 China Institute Of Software Chinese Academy Of Sciences Science And Technology On Integrated Information System Laboratory Beijing100190 China

ISBN: (纸本)9798350361445

Low-light image enhancement is an important issue in digital image processing area. However, in practice, it is still a difficult problem, especially how to balance computational cost and restore result. To tackle this difficulty, in this paper, we present a fast curve transition based low-light image enhancement model and propose a new lightweight curve estimation model. Specifically, an extremely lightweight curve estimation network based on parameter curve model is proposed, called LCE. Compared with the traditional method, called Zero-DCE, which utilizes iterative polynomial interpolation, the proposed can estimate the translate curve non-recursively. Benefitting from intuitive and simple nonlinear curve mapping as well as the lightweight curve estimation network, experimental results show that the proposed model is superior to the existing methods in terms of computational cost, while holds similar enhanced results on low-light images. © 2024 IEEE.

关键词： Image enhancement

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Heuristic Search with Cut Point Based Strategy for Critical Node Problem

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Journal of Computer science & technology 2024年第6期39卷 1328-1340页

作者： Zhi-Han Chen Shao-Wei Cai Jian Gao Shi-Ke Ge Chan-Juan Liu Jin-Kun Lin School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 101408China Key Laboratory of System Software(Chinese Academy of Sciences)and State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China College of Information Science and Technology Northeast Normal UniversityChangchun 130024China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China SeedMath Technology Limited Beijing 100086China

The critical node problem(CNP)aims to deal with critical node identification in a graph,which has extensive applications in many *** CNP is a challenging task due to its computational complexity,and it attracts much attention from both academia and *** this paper,we propose a population-based heuristic search algorithm called CPHS(Cut Point Based Heuristic Search)to solve CNP,which integrates two main *** first one is a cut point based greedy strategy in the local search,and the second one involves the functions used to update the solution pool of the ***,a mutation strategy is applied to solutions with probability based on the overall average similarity to maintain the diversity of the solution *** are performed on a synthetic benchmark,a real-world benchmark,and a large-scale network benchmark to evaluate our *** with state-of-the-art algorithms,our algorithm has better performance in terms of both solution quality and run time on all the three benchmarks.

关键词： local search cut point heuristic search critical node problem

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Non-Autoregressive Sign Language Translation: A Transformer Encoder-only Approach for Enhanced Multimodal Integration 24

Non-Autoregressive Sign Language Translation: A Transformer ...

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2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024

作者： Du, Yao Xie, Pan Peng, Taiying Hu, Xiaohui School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400717116

Sign language translation aims to transform continuous sign language videos into text, thereby mapping sign language actions onto natural language. Current research primarily focuses on the Transformer's encoder-decoder architecture, employing an autoregressive (AR) approach for SLT. This one-by-one token generation method has a slow speed to translate. We design a Non-Autoregressive Sign Language Translation (NA-SLT) model based on Transformer Encoder. The proposed model consists of three components: The text-encoder, sign-encoder, and sign-Text-encoder. Both the text-encoder and sign-encoder utilize the Encoder to model single-modality data. The sign-Text-encoder effectively engages with multi-modal data, producing high-quality corresponding text after minimal refinement iterations. Moreover, during model training, we devise a double-mask strategy. In addition to traditional random masking, the schedule allocates mask probabilities based on each word's frequency, ensuring long-Tail tokens have an increased chance of being masked and receiving extensive training. We evaluate the model on PHOENIX-2014T, and the results indicate that NA-SLT approaches the performance of AR models on BLEU (21.06) and ROUGE(49.59) metrics while significantly improving translation speed (5.4x-7.9x). © 2024 ACM.

关键词： Metadata

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An Improved SAR Image Simulation Method for the Lunar Surface using Refined Terrain Modeling Derived from LROC Stereo Data 8

An Improved SAR Image Simulation Method for the Lunar Surfac...

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8th International Conference on Control, Robotics and Cybernetics, CRC 2023

作者： Zhou, Ya-Ting Zhou, Yongsheng Zhu, Bo Ma, Fei Yin, Qiang Zhang, Fan Beijing University of Chemical Technology College of Information Science and Technology Beijing China Chinese Academy of Sciences Science and Technology on Integrated Information System Laboratory Institute of Software Beijing China

ISBN: (纸本)9798350330588

Extensive simulation sample data are crucial for the intelligent interpretation of synthetic aperture radar (SAR) data of the lunar surface using deep learning techniques. Existing simulation methods inadequately capture the diverse features of lunar terrains, including complex craters, impact basins, and domes. This research presents an improved SAR image simulation method for the lunar surface using refined terrain modeling derived from Lunar Reconnaissance Orbiter Camera (LROC) stereo data, encompassing a wide range of lunar terrains. Firstly, morphology models for typical lunar terrains, such as simple craters, complex craters, impact basins, and domes, are established by combining fundamental principles of lunar terrain formation with diverse terrain features identified in LROC data. Then, the Advanced Two-Scale Model (ATSM) is utilized for its broad applicability in precisely calculating the scattering characteristics of the lunar surface. Finally, the SAR echo fast time-frequency domain simulation algorithm and the Chirp Scaling (CS) imaging algorithm are used to generate simulated SAR images of the lunar surface. The results demonstrate that the proposed method can simulate realistic lunar surface SAR images, offering a novel approach for constructing a comprehensive lunar surface SAR dataset and enhancing the application of deep learning in interpreting lunar surface data. © 2024 IEEE.

关键词： Synthetic aperture radar

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Trusted Polarimetric Feature Fusion for Polsar Image Classification

Trusted Polarimetric Feature Fusion for Polsar Image Classif...

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2023 SAR in Big Data Era, BIGSARDATA 2023

作者： Han, Fangzhou Zhang, Lamei Dong, Hongwei Harbin Institute of Technology Department of Information Engineering Harbin150001 China Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory Beijing China

ISBN: (纸本)9798350326239

Benefiting from the rapid advancements in Deep Learning (DL), data-driven features exhibit remarkable separability in PolSAR image classification. However, data-driven features are challenging to interpret and susceptible to resolution and noise. On the contrary, polarimetric features based on the target decomposition not only have high robustness, but also has physical interpretability. Consequently, researchers have begun to explore ways to integrate both types of features to achieve enhanced performance, which typically concat the data-driven features and polarimetric features directly. However, polarimetric features are not stable in separability, which is because the inherent defects of the target decomposition algorithm. To mitigate the adverse effects of low-quality features, this paper proposes a trusted polarimetric feature fusion (TPFF). The new paradigm calculates the confidence of features and employs a novel feature fusion method. The proposed method has been tested on the EMISAR Foulum dataset, with experimental results demonstrating improved classification performance and robustness. © 2023 IEEE.

关键词： Image classification

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Application of Fish Swarm Algorithm in Space Based Dynamic Multicast Routing

Application of Fish Swarm Algorithm in Space Based Dynamic M...

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2022 International Conference on Electronic information Engineering and Computer science, EIECS 2022

作者： Duan, Qi-Feng Liu, Li-Xiang Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory University of Chinese Academy of Sciences Beijing China

By applying the fish swarm algorithm to the generation of the multicast tree under the dynamic satellite network, a method for generating the multicast tree with good adaptability and inheritance was found, and the ge... 详细信息

ISBN: (纸本)9781510663312

关键词： Trees (mathematics)

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GrammarT5: Grammar-integrated Pretrained Encoder-Decoder Neural Model for Code 24

GrammarT5: Grammar-Integrated Pretrained Encoder-Decoder Neu...

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44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Zhu, Qihao Liang, Qingyuan Sun, Zeyu Xiong, Yingfei Zhang, Lu Cheng, Shengyu MoE Scs Peking University Beijing China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Zte Corporation Chengdu China

ISBN: (纸本)9798400702174

Pretrained models for code have exhibited promising performance across various code-related tasks, such as code summarization, code completion, code translation, and bug detection. However, despite their success, the majority of current models still represent code as a token sequence, which may not adequately capture the essence of the underlying code structure. In this work, we propose GrammarT5, a grammar-integrated encoder-decoder pretrained neural model for code. GrammarT5 employs a novel grammar-integrated representation, Tokenized Grammar Rule Sequence (TGRS), for code. TGRS is constructed based on the grammar rule sequence utilized in syntax-guided code generation and integrates syntax information with code tokens within an appropriate input length. Furthermore, we suggest at-taching language flags to help GrammarT5 differentiate between grammar rules of various programming languages. Finally, we in-troduce two novel pretraining tasks-Edge Prediction (EP), and Sub-Tree Prediction (STP) to learn syntactic information. Experiments were conducted on five code-related tasks using eleven datasets, demonstrating that GrammarT5 achieves state-of-the-art (SOTA) performance on most tasks in comparison to models of the same scale. Additionally, the paper illustrates that the proposed pretraining tasks and language flags can enhance GrammarT5 to better capture the syntax and semantics of code. © 2024 ACM.

关键词： Semantics

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