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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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Interactive Human-in-the-loop Coordination of Manipulation Skills Learned from Demonstration 39

Interactive Human-in-the-loop Coordination of Manipulation S...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Guo, Meng Buerger, Mathias Peking Univ Coll Engn Beijing Peoples R China Bosch Ctr Artificial Intelligence BCAI Renningen Germany

ISBN: (纸本)9781728196817

Learning from demonstration (LfD) provides a fast, intuitive and efficient framework to program robot skills, which has gained growing interest both in research and industrial applications. Most complex manipulation tasks are longterm and involve a set of skill primitives. Thus it is crucial to have a reliable coordination scheme that selects the correct sequence of skill primitive and the correct parameters for each skill, under various scenarios. Instead of relying on a precise simulator, this work proposes a human-in-the-loop coordination framework for LfD skills that: builds parameterized skill models from kinesthetic demonstrations;constructs a geometric task network (GTN) on-the-fly from human instructions;learns a hierarchical control policy incrementally during execution. This framework can reduce significantly the manual design efforts, while improving the adaptability to new scenes. We show on a 7-DoF robotic manipulator that the proposed approach can teach complex industrial tasks such as bin sorting and assembly in less than 30 minutes.

关键词： automation Service robots Robot kinematics Education Manuals Manipulators Human in the loop

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Personalized Car Following for Autonomous Driving with Inverse Reinforcement Learning 39

Personalized Car Following for Autonomous Driving with Inver...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhao, Zhouqiao Wang, Ziran Han, Kyungtae Gupta, Rohit Tiwari, Prashant Wu, Guoyuan Barth, Matthew J. Toyota Motor North Amer R&D InfoTech Labs Mountain View CA 94043 USA Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92507 USA Univ Calif Riverside Ctr Environm Res & Technol Riverside CA 92507 USA

ISBN: (纸本)9781728196817

Driving automation is gradually replacing human driving maneuvers in different applications such as adaptive cruise control and lane keeping. However, contemporary driving automation applications based on expert systems or predefined control strategies are not in line with individual human driver's preference. To overcome this problem, we propose a Personalized Adaptive Cruise Control (P-ACC) system that can learn the driver's car-following preferences from historical data using model-based maximum entropy Inverse Reinforcement Learning (IRL). Once activated in real-time, the P-ACC system first classifies the driver type and the weather type (at that moment). The vehicle is then controlled using the pre-trained IRL model on the cloud of the associated class. The personalized IRL model on the cloud will be updated as more human driving data is collected from various scenarios. Numerical simulation with real-world naturalistic driving data shows that, the accuracy of reproducing the real-world driving profile improves up to 30.1% in terms of speed and 36.5% in terms of distance gap, when P-ACC is compared with the Intelligent Driver Model (IDM). Game engine-based human-in-the-loop simulation demonstrates that, the takeover frequency of the driver during the usage of P-ACC decreases up to 93.4%, compared with that during the usage of IDM-based ACC.

关键词： automation Clouds Reinforcement learning Numerical simulation Data models Real-time systems Numerical models

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Stochastic VI-TIR Image Fusion Method and Verification Using Lightweight models 24

Stochastic VI-TIR Image Fusion Method and Verification Using...

引用

4th international conference on Artificial Intelligence, automation and Algorithms

作者： Sun, Yun Wang, Jie Cui, Xutai Xu, Fufang Acad Mil Sci Natl Innovat Inst Def Technol Beijing Peoples R China

ISBN: (纸本)9798400717840

The recognition of only visible image is difficult to meet the rapidly developing application requirements of increasingly complex application scenarios in automatic driving and industrial automation. More and more researchers have turned their attention to the field of multi-modal image fusion. In this paper, a stochastic fusion method of multi-modal image using infrared image as mask is introduced. Considering the deployment requirements of edge computing platforms, the recognition rate of fused image and non-fused image is compared and verified on several lightweight models. The results show that compared with non-fused images, the fusion image provided by this method can improve the recognition rate by 5%similar to 7% and reduce the storage cost of these fused images greatly(uint8 reduced to uint3). In a meanwhile, the accuracy of classification models for the stochastic VI-TIR fusion image is roughly equivalent to that of fusion images acquired by TarDAL and LDFusion methods. The results demonstrates that the stochastic fusion method is conducive to multi-modal image fusion and local recognition on the edge computing platforms.

关键词： Multi-modal image fusion Stochastic fusion Binary neural network Edge computing platform

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Tunes by Technology: A Comprehensive Survey of Music Generation models 1

Tunes by Technology: A Comprehensive Survey of Music Generat...

引用

1st IEEE international conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Pathariya, Mohammed Johar Basavraj Jalkote, Pratyush Patil, Aniket Maharudra Ashok Sutar, Abhishek Ghule, Rajashree L. VPKBIET Baramati Ai & Ds Department Pune India

ISBN: (纸本)9798350372748

Notable developments in Artificial Intelligence-based music production have brought about a transformation in the field of musical composition. The aim of this article is to give a summary of the different models and techniques used in AI-driven music production. It explores the need for these developments, highlighting the shortcomings of traditional music production methods, which often call for a significant investment of time and expertise and presenting Artificial Intelligence (AI) as a revolutionary solution to these challenges. Several models are examined in the review, including Transformer Architecture, Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN) and many more. This study aims to give a comprehensive overview of Music generating techniques and their potential to improve the efficiency, accessibility, and collaborative nature of music creation within the musical environment. © 2024 IEEE.

关键词： Long short-term memory

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Algorithms in Surgical Action Recognition: A Survey

Algorithms in Surgical Action Recognition: A Survey

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2024 IEEE international conference on Real-Time Computing and robotics, RCAR 2024

作者： Bian, Gui-Bin Peng, Yaqin Zhang, Li Li, Jun Li, Zhen Institute of Automation Chinese Academy of Sciences Beijing China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China

ISBN: (纸本)9798350372601

Machine learning and medical image processing technology continue to promote the advancement of scene perception method. Among the field of scene perception, surgical action recognition is finer-grained and conductive to surgical guidance and evaluation, promoting the realization of automatic surgical robot. In this work, an overall review of surgical action recognition is provided, including the methods, datasets, metrics, and prospects. From the perspective of labeling degree of datasets, the methods are categorized: supervised learning, weakly-supervised or unsupervised learning, and reinforcement learning. Correspondingly, their algorithms, performances and contributions are concluded. Then, the commonly used datasets and metrics in surgical action recognition are summarized. Finally, the prospect is discussed. This survey highlights the relevant corner stones and latest researches in the field, covers the widely used datasets and metrics, offers valuable insight into the prospects, and aims to gain more attention of researchers to surgical action recognition. © 2024 IEEE.

关键词： Self-supervised learning

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Machine Learning models for Gait Phases Detection Using Surface Electromyography Signals 17th

Machine Learning Models for Gait Phases Detection Using Surf...

引用

17th international conference on Intelligent robotics and Applications

作者： Feng, Zhiyang Jiang, Zhengxuan Liu, Huayue Wang, Wenkong Wang, Yiqi Lu, Chang Ma, Xin Li, Yibin Song, Rui Zhang, Huanghe Shandong Univ Sch Control Sci & Engn Ctr Robot Jinan Shandong Peoples R China Univ Warwick Coventry W Midlands England Heidelberg Univ Fac Med Heidelberg Germany Heidelberg Univ Hosp Inst Computat Biomed Heidelberg Germany

ISBN: (纸本)9789819607853;9789819607860

Accurate gait phase detection is a fundamental technology for wearable rehabilitation robots, enabling them to deliver effective, safe, and personalized assistance that closely aligns with the therapeutic needs and safety requirements of users. Unlike force sensors or inertial measurement units, surface electromyography (sEMG) signals establish a direct link to the wearer's motion intentions, making them ideally suited for real-time applications. This study investigates the utilization of sEMG signals to identify gait phases using three types of machine learning models: k-nearest neighbors (KNN), support vector machines (SVM), and artificial neural networks (ANN). The models are trained on a publicly available dataset (i.e., SIAT-LLMD), which consists of data from 40 healthy individuals and utilizes three training methods: holdout, 10-fold cross-validation, and leave-one-subject-out cross-validation. ANN achieved the best results, demonstrating an overall accuracy of 92.6%+/- 2.6% for detecting stance and swing phases, and 94.4%+/- 4.2% for detecting five gait sub-phases (i.e., stance flexion phase, stance extension phase, preswing phase, swing flexion phase, and swing extension phase).

关键词： EMG signals Gait phase detection Machine learning Artificial neural networks

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

引用

4th international conference on Industrial automation, robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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Adaptive Dynamic Sliding Mode Control of Soft Continuum Manipulators 39

Adaptive Dynamic Sliding Mode Control of Soft Continuum Mani...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Kazemipour, Amirhossein Fischer, Oliver Toshimitsu, Yasunori Wong, Ki Wan Katzschmann, Robert K. Swiss Fed Inst Technol Zurich Switzerland Sapienza Univ Rome Rome Italy Univ Tokyo Tokyo Japan

ISBN: (纸本)9781728196817

Soft robots are made of compliant materials and perform tasks that are challenging for rigid robots. However, their continuum nature makes it difficult to develop model-based control strategies. This work presents a robust model-based control scheme for soft continuum robots. Our dynamic model is based on the Euler-Lagrange approach, but it uses a more accurate description of the robot's inertia and does not include oversimplified assumptions. Based on this model, we introduce an adaptive sliding mode control scheme, which is robust against model parameter uncertainties and unknown input disturbances. We perform a series of experiments with a physical soft continuum arm to evaluate the effectiveness of our controller at tracking task-space trajectory under different payloads. The tracking performance of the controller is around 38% more accurate than that of a state-of-the-art controller, i.e., the inverse dynamics method. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of segments. With this control strategy, soft robotic object manipulation can become more accurate while remaining robust to disturbances.

关键词： Adaptation models Uncertain systems automation Control design Soft robotics Trajectory Task analysis

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Surface Quality Characterization and Modeling for Printed 2D Products Using Printing Primitives 20

Surface Quality Characterization and Modeling for Printed 2D...

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IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Gu, Minghao Ruiz, Cesar Huang, Qiang Univ Southern Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90007 USA Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

ISBN: (纸本)9798350358513;9798350358520

Surface quality characterization is essential for guiding design and manufacturing, as well as ensuring product functionality. Although a wealth of literature has been developed for surface quality characterization in mass production, it faces unique challenges in additive manufacturing (AM) due to one-off fabrication of a large variety of products with complex geometries. Surface quality characteristics can vary with built geometries and covariates such as size, built location, and orientation, which makes existing statistical characterization methods not adaptable to frequent design changes in AM. To address these challenges, we proposed a novel surface quality characterization and modeling approach based on printing primitives. We partition a 2D design into a finite amount of printing primitives. The in-plane surface quality patterns on each type of primitives are characterized through tensorproduct modeling with integration of domain knowledge to reflect the impact of individual covariates. Simulation studies demonstrate the capability of the proposed model for learning the stochastic process governing the surface quality distribution. We also illustrate the application of the proposed model for analyzing real printed samples produced by an AM process.

关键词： Stochastic models

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Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling 39

Unseen Object Amodal Instance Segmentation via Hierarchical ...

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IEEE international conference on robotics and automation (ICRA)

作者： Back, Seunghyeok Lee, Joosoon Kim, Taewon Noh, Sangjun Kang, Raeyoung Bak, Seongho Lee, Kyoobin Gwangju Inst Sci & Technol GIST Sch Integrated Technol Sit Cheomdan Gwagiro 123 Gwangju 61005 South Korea

ISBN: (纸本)9781728196817

Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limited to segmenting the only visible regions of unseen objects. For robotic manipulation in a cluttered scene, amodal perception is required to handle the occluded objects behind others. This paper addresses Unseen Object Amodal Instance Segmentation (UOAIS) to detect 1) visible masks, 2) amodal masks, and 3) occlusions on unseen object instances. For this, we propose a Hierarchical Occlusion Modeling (HOM) scheme designed to reason about the occlusion by assigning a hierarchy to a feature fusion and prediction order. We evaluated our method on three benchmarks (tabletop, indoors, and bin environments) and achieved state-of-the-art (SOTA) performance. Robot demos for picking up occluded objects, codes, and datasets are available at https://***/view/uoais.

关键词： Codes automation Predictive models Benchmark testing Task analysis Robots

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