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Plasti-Former: An Optimal Transport Enhanced Transformer Based Inverse Dynamics Model for Plasticine Shape Manipulation

Plasti-Former: An Optimal Transport Enhanced Transformer Bas...

引用

international conference on Control, automation and robotics (ICCAR)

作者： Dongxu Duan Houde Liu Chongkun Xia Mingqi Shao Yu Liang Bin Liang Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

Plasticine shape manipulation (PSM) is essential in food processing and ceramic production. However, it is challenging since high degrees of freedom of plasticine make it difficult for state representation and planning. Some previous approaches focus on optimizing actions using gradients provided by differentiable particle-based simulators, which still have a noticeable gap compared to real scenarios. In this paper, we use a long roller and present a specific inverse dynamics model (Plasti-Former) to perform PSM. The input to Plasti-Former consists of the provided sub-goals and the current point cloud state of the plasticine; the output is the robot's actions. Plasti-Former comprises a rolling direction prediction module and a rolling position prediction module. In the first module, we integrate optimal transport theory with a PointNet-based model trained with 30k virtual data and 6k actual data to predict the roller's rolling direction θ. In the second module, we predict the start rolling position (x, y) using an attention-based model. Note that the input of this attention-based model is a discretized spatial sequence uniquely designed by us, and the features of each subspace are constructed using the optimal transport vectors. We experimentally evaluate Plasti-Former on six representative sequential plasticine shape manipulation tasks and show that Plasti-Former outperforms state-of-the-art approaches in simu-lation. We also conducted the experiments in the real world, demonstrating a promising sim-to-real effect.

关键词： Point cloud compression Shape Production Predictive models Transformers Vectors Planning

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An Empirical Evaluation of Out-of-Distribution Detection Using Pretrained Language models

An Empirical Evaluation of Out-of-Distribution Detection Usi...

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international conference on Control and Robots (ICCR)

作者： Byungmu Yoon Jaeyoung Kim Gachon University Seongnam-si Gyeonggi-do Korea

In Natural Language Processing (NLP) tasks, detecting out-of-distribution (OOD) samples is essential to safely deploy a language model in real-world problems. Recently, several studies report that pre-trained language models (PLMs) accurately detect OOD data compared to LSTM, but we empirically find that PLMs show sub-par OOD detection performance when (1) OOD samples have similar semantic representation to in-distribution (IND) samples and (2) PLMs are finetuned under data scarcity settings. To alleviate above issues, state-of-the-art uncertainty quantification (UQ) methods can be used, but the comprehensive analysis of UQ methods with PLMs has received little consideration. In this work, we investigate seven UQ methods with PLMs and show their effectiveness in the text classification task.

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Human-Like Dexterous Manipulation for the Anthropomorphic Hand-Arm Robotic System via Teleoperation 16th

Human-Like Dexterous Manipulation for the Anthropomorphic ...

引用

16th international conference on Intelligent robotics and Applications, ICIRA 2023

作者： Huang, Yayu Wang, Zhenghan Shen, Xiaofei Liu, Qian Wang, Peng Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences 999077 Hong Kong

ISBN: (纸本)9789819964918

Teleoperation has the potential to enable robots to replace humans in high-risk scenarios and catastrophic events, performing manipulation tasks efficiently and securely under human guidance. However, achieving human-like dexterous manipulation remains challenging, particularly for anthropomorphic hand-arm robotic systems with high degrees of freedom. Accurately capturing the operator’s motion and providing real-time intuitive feedback to enhance the sense of telepresence place substantial demands on human-robot perception and interaction. Moreover, the inherent physical and functional differences between anthropomorphic hand-arm robots and humans pose challenges in ensuring the accuracy and reliability of dexterous teleoperation. To overcome these challenges, we present an integrated approach involving an anthropomorphic hand-arm robot system, a wearable motion capture and force feedback system, and a set of motion mapping and force mapping methods. We conducted experiments on both simulation and real-world platforms to evaluate the usability and effectiveness of our proposed approach, with the results demonstrating significant advancements in achieving human-like dexterity via teleoperation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Remote control

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Evaluating the Performance of Liquefaction Prediction models on the Basis of Fuzzy Sets Theory

Evaluating the Performance of Liquefaction Prediction Models...

引用

international conference on Control, Instrumentation, and automation

作者： Maedeh Mehrvarzan Ali Derakhshani Department of Civil Engineering Faculty of Engineering Shahed University Tehran Iran

Several Prediction models have been proposed to evaluate liquefaction phenomenon using numerical and experimental methods. The performance of each model depends on the accuracy and reliability of its output. Errors in the outputs are caused by the uncertainties in the input data. The existing uncertainty in nature and neglecting this issue in most of the presented models has led to unreliable response of these models. In this study, two liquefaction estimation models of Zhang et al. (2016) and Jafari Avval and Derakhshani (2020) are investigated in terms of the impact of uncertainty. Also, a sensitivity analysis is conducted for reliability comparison purposes.

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Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System 1

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international conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Asy’ari, Muhammad Zacky Filbert, Sebastian Sukra, Zener Lie Automotive and Robotics Program Computer Engineering Department BINUS ASO of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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STOW-Net: Spatio-Temporal Operation Based Deep Learning Network for Classifying Wavelet Transformed Motor Imagery EEG Signals 38

STOW-Net: Spatio-Temporal Operation Based Deep Learning Netw...

引用

38th IEEE Region 10 conference, TENCON 2023

作者： Rana, Md. Shohel Fattah, Shaikh Anowarul Saquib, Mohammad Institute of Robotics and Automation Buet Dhaka Bangladesh Buet Department of Eee Dhaka Bangladesh The University of Texas Department of Ee DallasTX United States

ISBN: (纸本)9798350302196

Brain-computer interface (BCI) systems rely on capturing characteristics of human brain activity from the electroencephalography (EEG) signals, especially for the reliable classification of motor imagery tasks. For multi-channel EEG signals, it is crucial to precisely capture the spatio-temporal variation along with the frequency characteristics. Hence, instead of directly operating on raw EEG data, in this paper, discrete wavelet transform (DWT) is first applied to the motor-imagery multi-channel EEG data and then a deep learning architecture is designed incorporating spatial-temporal operations, which operates on the DWT-transformed EEG signal. In the proposed architecture, temporal convolution followed by spatial convolution is performed on the DWT-operated MI-EEG signal, and this part is termed as SAT-net. Next, by considering all channels together convolutional operation is performed to reduce the number of channels and this part is termed as SOC-net. Finally, a fully connected layer is used to classify the MI-EEG data from the derived feature vector. Extensive experimentation is performed on multiple subjects taken from the MI-based EEG dataset BCI Competition IV 2a. It is found that the proposed model offers a classification accuracy of 84.65%, consistently providing better classification performance than that obtained by some state-of-the-art methods. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Dense Rebar End Face Segmentation based on YOLACT_REBAR Instance Segmentation Network 15

Dense Rebar End Face Segmentation based on YOLACT_REBAR Inst...

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15th international conference on Graphics and Image Processing, ICGIP 2023

作者： Yuan, Li Zhou, Yu Deng, Hua Ouyang, Long School of Automation and Electrical Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing Beijing100083 China Recast Metallurgy Technology CO. LTD Chuangxin Road Taodu Industry Zone Yixing Jiangsu Province Wuxi214221 China

ISBN: (纸本)9781510675018

Automatic rebar counting in the bundled steel bar image require urgent solution in the steel and construction application scenarios. Traditional image processing methods perform badly because the rebar end faces are in irregular shape and highly dense. Focusing on the characteristics of dense small objects in the bundled rebar images, this paper proposes YOLACT_REBAR instance segmentation network. We make improvements from two aspects: network structure and inference process. For network structure improvement, in view of the characteristics of the steel bar end face being a small target, we optimize the original model by connecting the second layer of its backbone network to FPN (Feature Pyramid Network) to obtain larger feature maps, thereby enhancing the model's accuracy in dense small target segmentation. For the inference process, in view of the dense characteristics of steel bar end faces, traditional Non-Maximum value Suppression is used to replace the fast Non-Maximum value Suppression, and the IOU (Intersection-Over-Union) deduplication strategy is used to remove redundant bounding boxes, thereby reducing the network's false detection. Experimental results on self-constructed dataset show that compared with the original YOLACT model and other object detection models, our proposed model demonstrate improved Precision, Recall and mIOU performance. The proposed YOLACT_REBAR model can make a precise segmentation of each rebar end face, which will facilitate subsequent applications such as rebar counting and automatic plate welding. © 2024 SPIE. All rights reserved.

关键词： Bars (metal)

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NLOS Error Suppression Method for UWB Indoor Positioning based on TDOA

NLOS Error Suppression Method for UWB Indoor Positioning bas...

引用

Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Pengwei Yang Lei Wu Dingjin Huang Equipment Management and Support College Engineering University of PAP Xi'an China School of Automation Beijing Institute of Technology Beijing China School of Optoelectronic Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

As people's requirements for indoor positioning accuracy continue to increase, indoor wireless positioning technology has developed rapidly. Among the many wireless positioning technologies, ultra-wideband (UWB) wireless positioning has quickly become a hot topic in today's indoor positioning research due to its strong anti-multipath interference ability, low power consumption, and high positioning accuracy. However, due to the complex and changeable indoor environment, UWB devices are interfered with by various factors in this environment, resulting in the inevitable NLOS (non-line-of-sight) error in the wireless positioning system, which hugely affects the positioning accuracy of the positioning system. For the problem of NLOS propagation in indoor pedestrian positioning due to received signal strength indication (RSSI), this paper proposes a locating algorithm that predicts RSSI using a motion model and corrects NLOS measurements. Firstly, the pedestrian's position and RSSI are predicted using a motion model, and NLOS errors are detected and corrected. Then, the pedestrian's activity range is estimated using step length prediction, and an improved trilateration algorithm is used for positioning. Finally, the Fang-unscented Kalman filter is applied to optimize the positioning results and obtain an optimized estimation of the position. Simulation experiments indicate that this method can validly improve locating accuracy, suppress large NLOS errors, and is an effective positioning method in NLOS environments.

关键词： Wireless communication Accuracy Pedestrians Predictive models Position measurement Prediction algorithms Motion measurement Received signal strength indicator Kalman filters Ultra wideband technology

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Wire Grid Scatterer Modeling: TALGAT Verification

Wire Grid Scatterer Modeling: TALGAT Verification

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international Russian automation conference (RusAutoCon)

作者： Tuan Phuong Dang Adnan F. Alhaj Hasan Talgat R. Gazizov Department of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Complex scattering structures have many practical applications, therefore they are often analyzed using many numerical methods integrated in different systems, one of which is TALGAT. However, until now there has been no work on modeling complex scattering structures using this system, therefore, in this paper the results of analyzing plate scattering structures using TALGAT are presented and verified. Electromagnetic scattering characteristics such as radar cross section and backscattering cross section are considered. The accuracy of the TALGAT results obtained by the method of moments with pulse basis functions is verified by comparing them with those obtained analytically, experimentally and numerically using other methods published in previous works. These comparisons demonstrated the ability of TALGAT to give accurate results when analyzing complex wire grid scatterers.

关键词： Analytical models Accuracy Radar cross-sections Electromagnetic scattering Numerical models Wire Time-domain analysis Method of moments Finite difference methods Backscatter

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Post-hoc Explainable Reinforcement Learning Using Probabilistic Graphical models 1

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11th international Advanced Computing conference, IACC 2021

作者： Deshpande, Saurabh Walambe, Rahee Kotecha, Ketan Jakovljević, Marina Marjanović Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune412 115 India Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9783030955021

ISBN: (纸本)9783030955014

Reinforcement learning (RL) has recently enjoyed significant success in games, robotics, bioinformatics, etc. Soon, it will not be uncommon to see AI models employing RL agents integrated with various hardware and software solutions. Due to its generality and robustness, RL is applied in several disciplines such as game theory, control theory, multi-agent systems, swarm intelligence, robotics, and NLP. Despite these advances and successes, reinforcement learning faces many challenges for real-world adoption. Some of the major difficulties being, operator’s trust and ability of an agent to explain the actions taken in a human-understandable manner. Traditionally the AI systems are black-box models. With the advent of various legal regulations worldwide, notably the European General Data Protection Regulation (GDPR) [29], it has started becoming mandatory that the AI models be transparent, interpretable, and secure. If an RL agent can effectively and accurately explain the actions carried out by the RL system to the observers/operators, it will be a tangible step towards developing the ART (accountable, reliable and trustworthy) RL agent. This can effectively facilitate the adoption of RL systems in real-world domains. Various explainable AI (XAI) methods have been reported in the literature. However, there is a considerable lacuna in the availability of Explainable RL (XRL) methods. This paper introduces a novel RL algorithm agnostic approach of generating human-understandable explanations using the probabilistic graphical model. This method is based on Probabilistic Graphical models (PGM) [36]. It is algorithm agnostic in that it is not dependent on any specific RL method and can be integrated with any RL algorithm. We also introduce a PGM model, which is learned along with an agent’s training via classic methods and used for generating explanations at run time. Specific case studies are considered, and results are presented which demonstrate our approach. Our exper

关键词： Reinforcement learning

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