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arXiv 2023年

作者： Ak, Abdullah Cihan Aksoy, Eren Erdal Sariel, Sanem Artificial Intelligence and Robotics Laboratory Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Turkey School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Halmstad Sweden

Robots are more capable of achieving manipulation tasks for everyday activities than before. But the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Beyond that, in unstructured environments, it is not easy to enumerate all possible failures beforehand. In the context of safe skill manipulation, we reformulate skills as base and failure prevention skills where base skills aim at completing tasks and failure prevention skills focus on reducing the risk of failures to occur. Then, we propose a modular and hierarchical method for safe robot manipulation by augmenting base skills by learning failure prevention skills with reinforcement learning, forming a skill library to address different safety risks. Furthermore, a skill selection policy that considers estimated risks is used for the robot to select the best control policy for safe manipulation. Our experiments show that the proposed method achieves the given goal while ensuring safety by preventing failures. We also show that with the proposed method, skill learning is feasible, novel failures are easily adaptable, and our safe manipulation tools can be transferred to the real environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

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MTPA-based Haptic Feedback Controller for Master Manipulator in Minimally Invasive Surgery

MTPA-based Haptic Feedback Controller for Master Manipulator...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhao, Wenda Chen, Jian Hong, Yilun Liu, Hongbin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Hong Kong School of Biomedical Engineering and Imaging Sciences King's College London LondonSE1 7EU United Kingdom

ISBN: (纸本)9781665481090

Haptic feedback is critical for teleoperation in surgical robots, particularly in Natural Orifice Transluminal Endoscopic Surgery (NOTES). This paper introduces a haptic controller designed to enhance force generation efficiency and resolution, surpassing traditional motor control frameworks. We propose a Maximum Torque Per Ampere (MTPA)-based Field-Oriented Control (FoC) strategy for haptic kinesthetic feedback controller, optimizing the performance of a master manipulator's joint with a brushless DC (BLDC) motor under stalled and ultra-slow conditions. This approach enables a master manipulator with three degrees of freedom (DoF) for force feedback. Its effectiveness is validated through simulator and surgical trials, demonstrating improved haptic feedback in surgical applications. © 2024 IEEE.

关键词： Brushless DC motors

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PediatricsGPT: large language models as Chinese medical assistants for pediatric applications 24

PediatricsGPT: large language models as Chinese medical assi...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Dingkang Yang Jinjie Wei Dongling Xiao Shunli Wang Tong Wu Gang Li Mingcheng Li Shuaibing Wang Jiawei Chen Yue Jiang Qingyao Xu Ke Li Peng Zhai Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China and Cognition and Intelligent Technology Laboratory Shanghai China Tencent Youtu Lab Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China and Cognition and Intelligent Technology Laboratory Shanghai China and Engineering Research Center of AI and Robotics Ministry of Education Shanghai China and AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798331314385

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. The project and data will be released at https://***/ydk122024/PediatricsGPT.

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Kinematic Parameter Calibration for Parallel Robot Based on Robust Estimation and PCR Algorithm

Kinematic Parameter Calibration for Parallel Robot Based on ...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Jin Wang Renge Zhang Weijun Hu Haiyun Zhang Guodong Lu Silu Chen Jingbo Luo State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Robotics Institute of Zhejiang University Hangzhou China School of Robotics Ningbo University of Technology Ningbo China Zhejiang Qiantang Robot and Intelligent Equipment Research Co. LTD Hangzhou China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Due to the existence of redundant identification parameters and parallel branch chains, the nonlinear error model of parallel robot is likely to produce multicollinearity, which will lead to the undesirable ill-conditioned identification matrix. Though PCR method can modify the ill-conditioned identification matrix, it is still susceptible to the gross error of the original data. In terms of these problems, this paper proposes a parameter calibration algorithm for parallel robot based on robust estimation and principal component regression (RE-PCR). Firstly, the linear identification model is established based on the closed-loop vector chain perturbation theory to improve the identification accuracy. On this basis, for the configuration types with redundant identification parameters and parallel branch chains, PCR is used to modify the ill-conditioned matrix and a robust estimation (RE) algorithm is designed to improve the resistance capacities of PCR to gross error data points. Finally, through the identification experiment based on 4PPa-2PaR parallel robot, the proposed algorithm demonstrates higher efficiency and stronger robustness in comparison with traditional methods.

关键词： Resistance Parallel robots Parameter estimation Perturbation methods Estimation Kinematics Vectors

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QuerySOD: A Small Object Detection Algorithm Based on Sparse Convolutional Network and Query Mechanism

QuerySOD: A Small Object Detection Algorithm Based on Sparse...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Junnian Li Jie Niu MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Although remarkable advances have been achieved in generic object detection, small object detection (SOD) remains challenging owing to small objects’ information loss and noisy representation caused by their non-uniform distribution. Their limited width and height, scale variations, and redundant computation make SOD hard. To overcome them, this work proposes a new SOD method based on sparse convolutional network (SCNet) and Query Mechanism called QuerySOD. First, an extended feature pyramid network is constructed for extracting feature maps of small objects with more regional details. Then, a Sparse Head is neatly designed by using SCNet for accelerating the interfering speed and obtaining weights of each layer. After that, a Query Mechanism is innovatively introduced for harvesting the benefit of sparse value feature maps from the Sparse Head. QuerySOD is evaluated on public benchmarks including COCO and VisDrone. Finally, we apply it on ‘Jinghai’ unmanned survey vehicles and receive excellent SOD performance from this real-world application.

关键词： Surveys Accuracy Object detection Seaports Lakes Feature extraction Prediction algorithms Convolutional neural networks Security Noise measurement

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RAPID & FASTNAV Projects: High-Speed Semi-Autonomous Rovers Enabling High Return Planetary Missions

RAPID & FASTNAV Projects: High-Speed Semi-Autonomous Rovers ...

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Space robotics (iSpaRo), International Conference on

作者： Matteo De Benedetti Steven Kay Jorge Ocón Raquel Jalvo Manuel Esquer Cerezo Augusto Gómez Eguíluz Mercedes Alonso Javier Ramos Fernandez Karl Buckley Robert Field Angus Cameron Vassilios Papantoniou Ares Papantoniou Carlos Perez del Pulgar Konstantinos Kapellos Martin Azkarate GMV NSL Ltd UK GMV Aerospace and Defence Spain Hellenic Technology of Robotics Athens Greece Department of Systems Engineering and Automation Space Robotics Laboratory Universidad de Málaga Málaga Spain Network Research Belgium Uccle Belgium European Space Agency European Space Research and Technology Centre (ESTEC) Noordwijk Netherlands

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

Previous Mars and Moon rover missions have highlighted a limitation in mobility, with surface rovers traversing only a few tens of meters per day, at speeds in the order of 10 cm/s. This limitation can be primarily attributed to the rover locomotion system from one side, and on the other by the lack/limited on-board autonomous *** address these limitations, ESA issued a series of activities on fast autonomous rovers. GMV is leading the RAPID and FASTNAV projects, aiming at developing a semi-autonomous rover capable of safely traversing lunar terrains at high speeds (around 1 m/s), using a visual navigation based Guidance, Navigation and Control (GNC) system. The RAPID project was the foundation of this work, in which the rover platform was designed, manufactured, and a first version of the on-board software was developed. The follow-up FASTNAV project builds upon the work of RAPID to establish an innovative multi-mode continuous drive GNC system. This new architecture aims to achieve an improved autonomy and faster speeds, with different guidance modes selected according to the terrain type, using a combination of techniques (AI and classical computer vision), while also improving the existing GNC *** paper shall present the experience obtained during the development of the rover platform and the GNC system across both activities. It shall provide an overview of the challenges and risks, the decisions made, a detailed technical overview of the GNC architecture and components, and the results observed during the analogue field tests.

关键词： Space vehicles Meters Visualization Computer vision Navigation Space missions Moon Computer architecture Control systems Software

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A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping

A Context-Enhanced Full-Resolution Floor Plan Segmentation N...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhengcai Cao Yiyang Sun Zhe Ma MengChu Zhou State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China College of Information Science and Technology Beijing University of Chemical Technology Beijing China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Topological semantic maps provide a practical solution to enhance indoor navigation for the Partially Sighted or Visually Impaired (PSVI). Segmenting indoor floor plans and extracting boundaries are key to constructing these maps. The existing methods exhibit low accuracy in segmentation. To achieve desired high segmentation accuracy, we introduce a Context-Enhanced Full-Resolution Network (CEFRN) for floor plan segmentation. It is designed to harness the shallow detailed features and inter-category contextual dependencies inherent in floor plans. CEFRN integrates modified residual blocks to capture the low-stage full-resolution features while maintaining its compactness. A position attention module is employed to refine the deep-stage contextual information. We also propose a two-dimensional deep supervision method to merge features from both stages, which significantly boosts the feature representation ability of CEFRN. Finally, a practical topological semantic mapping method for PSVI indoor navigation is introduced. Experimental results demonstrate that CEFRN’s segmentation accuracy well exceeds the state-of-the-art methods’. It can be used to well support accurate topological semantic mapping.

关键词： Accuracy Hospitals Indoor navigation Semantics Feature extraction Mobile robots Floors Intelligent robots Autonomous vehicles Drones

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High-compatibility Manipulation Method for Parallel In-situ Isolation of Microalgal Cells via Optoelectronic Tweezers

High-compatibility Manipulation Method for Parallel In-situ ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Shilong Qin Jiaxin Liu Yuxin Chen Haotian Yang Ziyi Zhao Qing Shi Qiang Huang Huaping Wang Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China School of Mechatronical Engineering Beijing Institute of Technology Intelligent Robotics Institute Beijing China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Isolating microalgae in in-situ liquid environment based on optoelectronic tweezers (OETs) enables providing specific species of microalgae in high-purity, thus has significant potential in environmental detection, aquaculture, bioenergy, and biomedicine development. However, the conventional OET method can only separate and extract one type of microalgae in a single-step operation and is challenging to utilize the opposite dielectrophoretic force when the cross frequency of microalgae is too high /low or even absent. In this study, a novel automated microalgae isolation method based on optoelectronic tweezers is proposed, which integrates two strategies: cluster manipulation and discretized synchronous manipulation. Firstly, an optoelectronic tweezer robot platform was built, the dielectrophoretic forces on microalgae and the factors affecting the dielectrophoretic forces were analyzed. Secondly, a cluster manipulation strategy based on light patterns dynamic locomotion structure scheme was designed to simultaneously separate microalgae subject to dielectrophoretic forces of different attributes and the same attribute. Finally, a discretized synchronous manipulation strategy based on visual feedback and PID controller was designed to realize the precise isolation of microalgae subjected to the same dielectrophoretic force and different dielectrophoretic forces. Experimental results demonstrated that the aforementioned method provides a feasible microalgae isolating method based on optoelectronic tweezers within 10 minutes, which has potential applications in the in-situ water contamination detection and treatment.

关键词： Visualization Liquids Force Dynamics Water pollution Real-time systems Dielectrophoresis

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

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arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Edge Detectors Can Make Deep Convolutional Neural Networks More Robust

arXiv

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arXiv 2024年

作者： Ding, Jin Zhao, Jie-Chao Sun, Yong-Zhi Tan, Ping Wang, Jia-Wei Ma, Ji-En Fang, You-Tong School of Automation and Electrical Engineering Key Institute of Robotics of Zhejiang Province Zhejiang University of Science and Technology Hangzhou310023 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China School of Electrical Engineering Zhejiang University Hangzhou310027 China

Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN’s robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., largely relying on the shape features, this paper first employs the edge detectors as layer kernels and designs a binary edge feature branch (BEFB for short) to learn the binary edge features, which can be easily integrated into any popular backbone. The four edge detectors can learn the horizontal, vertical, positive diagonal, and negative diagonal edge features, respectively, and the branch is stacked by multiple Sobel layers (using edge detectors as kernels) and one threshold layer. The binary edge features learned by the branch, concatenated with the texture features learned by the backbone, are fed into the fully connected layers for classification. We integrate the proposed branch into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate the BEFB is lightweight and has no side effects on training. And the accuracy of the BEFB integrated models is better than the original ones on all datasets when facing FGSM, PGD, and C&W attacks. Besides, BEFB integrated models equipped with the robustness enhancing techniques can achieve better classification accuracy compared to the original models. The work in this paper for the first time shows it is feasible to enhance the robustness of DCNNs through combining both shape-like features and texture features. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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