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4th Conference on Robot learning, CoRL 2020

作者： Nguyen, Son-Tung Oguz, Ozgur S. Hartmann, Valentin N. Toussaint, Marc Machine Learning & Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Group TU Berlin Germany

We present a self-supervised representation learning approach for visual reasoning and integrate it into a nonlinear program formulation for motion optimization to tackle sequential manipulation tasks. Such problems have usually been addressed by combined task and motion planning approaches, for which spatial relations and logical rules that rely on symbolic representations have to be predefined by the user. We propose to learn relational structures by leveraging visual perception to alleviate the resulting knowledge acquisition bottleneck. In particular, we learn constructing scene-graphs, that represent objects ("red box"), and their spatial relationships ("yellow cylinder on red box"). This representation allows us to plan high-level discrete decisions effectively using graph search algorithms. We integrate the visual reasoning module with a nonlinear optimization method for robot motion planning and verify its feasibility on the classic blocks-world domain. Our proposed framework successfully finds the sequence of actions and enables the robot to execute feasible motion plans to realize the given tasks. © 2020 Proceedings of machine learning Research. All rights reserved.

关键词： Motion planning

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CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

arXiv

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arXiv 2023年

作者： Zheng, Kaizhong Yu, Shujian Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an China Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands Machine Learning Group UiT - Arctic University of Norway Tromsø Norway

There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which, in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations α and β that encode, respectively, the causal and non-causal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and three large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence. The source code and implementation details of CI-GNN are freely available at GitHub repository (https://***/ZKZ-Brain/CI-GNN/). Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Implementing machine learning Approaches to Identify Fabricated Profiles

Implementing Machine Learning Approaches to Identify Fabrica...

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Science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： George Princess T Kothai G Heartlin Maria H Kayalvizhi Rajadurai Ajai MR Department of ADS St. Joseph's Institute of Technology Chennai India Department of CSE (Artificial Intelligence and Machine Learning) KPR Institute of Engineering and Technology Coimbatore Department of ECE Rajalakshmi Engineering College Chennai India Dept.of Robotics and Automation Easwari Engineering College Chennai India Department of IT St. Joseph's Institute of Technology Chennai India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The increase of fake news is a serious challenge in today's digital world. During the period of COVID-19 there is a lot of fake news about the COVID-19 virus has been spreading around the world, and it has also been believed among a lot of people, so a lot of conspiracy theories about the virus-like has been intentionally spread by the laboratory and some news is about supporting the zoonotic origin of the virus, likely originating from bats. Some of the news is about False Cures and Treatments, There were various claims about supposed cures for COVID-19, such as drinking bleach, consuming certain herbs, or taking specific vitamins. Health experts and authorities consistently warned against such unproven remedies. Thus it is really necessary to differentiate the fake profile from the genuine profile. This fake news is mostly spread through fake profiles. So it is necessary to differentiate fake profiles from genuine users. This paper introduces a method for the detection of fake profiles using machine learning techniques. The proposed system uses a diversity of features, to distinguish genuine profiles from fake. A dataset is employed to train and evaluate the machine learning algorithm. This dataset includes labeled examples of both pseudo (fake) and real profiles. This paper aims to increase the efficiency in the detection of genuine news from fake. This proposed system mainly focuses on stopping the spread of malicious activities through out the internet, since these activities is mostly spread through the fake account on the social media platforms..

关键词： COVID-19 machine learning algorithms Social networking (online) Supervised learning Focusing machine learning Forestry

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Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and learning

arXiv

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arXiv 2023年

作者： Han, Zhi Liu, Baichen Lin, Shao-Bo Zhou, Ding-Xuan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Mathematics and Statistics University of Sydney SydneyNSW2006 Australia

This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in enabling translation-equivalence, and pooling in its translation-invariance driven nature, we show that with similar number of free parameters, any deep fully connected networks (DFCNs) can be represented by DCNNs with zero-padding. This demonstrates that DCNNs with zero-padding is essentially better than DFCNs in feature extraction. Consequently, we derive universal consistency of DCNNs with zero-padding and show its translation-invariance in the learning process. All our theoretical results are verified by numerical experiments including both toy simulations and real-data running. © 2023, CC0.

关键词： Feature extraction

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Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Tuscher Julian Hörz Danny Driess Marc Toussaint sereact Machine Learning and Robotics Lab University of Stuttgart Max-Planck Institute for Intelligent Systems Stuttgart Learning and Intelligent Systems TU Berlin

Robotic manipulation of unknown objects is an important field of research. Practical applications occur in many real-world settings where robots need to interact with an unknown environment. We tackle the problem of reactive grasping by proposing a method for unknown object tracking, grasp point sampling and dynamic trajectory planning. Our object tracking method combines Siamese Networks with an Iterative Closest Point approach for pointcloud registration into a method for 6-DoF unknown object tracking. The method does not require further training and is robust to noise and occlusion. We propose a robotic manipulation system, which is able to grasp a wide variety of formerly unseen objects and is robust against object perturbations and inferior grasping points.

关键词： Training Target tracking Automation Trajectory planning Perturbation methods Conferences Grasping

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Facial Expression Recognition Model Depending on Optimized Support Vector machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Context-Based Meta Reinforcement learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

arXiv

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arXiv 2024年

作者： Shokry, Ahmed Gomaa, Walid Zaenker, Tobias Dawood, Murad Menon, Rohit Maged, Shady A. Awad, Mohammed I. Bennewitz, Maren Humanoid Robots Lab Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Bonn Germany Cyber Physical Systems Lab Egypt Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt Mechatronics Department Ain Shams University Cairo Egypt

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot’s forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Co-Optimizing Robot, Environment, and Tool Design via Joint Manipulation Planning

Co-Optimizing Robot, Environment, and Tool Design via Joint ...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Marc Toussaint Jung-Su Ha Ozgur S. Oguz Learning & Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany Machine Learning & Robotics Lab University of Stuttgart Germany

Existing work on sequential manipulation planning and trajectory optimization typically assumes the robot, environment and tools to be given. However, in particular in industrial applications, it is highly interesting to ask, what would be an optimal robot design, tool shape, or robot station geometry for a particular ensemble of manipulation tasks. To tackle this problem we propose a formulation to jointly optimize over static design parameters and the sequential manipulation trajectory. We can include optimization objectives such as penalizing velocities (path length) and joint torques. Our evaluations show that design optimization can significantly improve on such metrics. For instance, in a wrench tool demonstration scenario we show that the shape of the wrench tool as well as design of the robot can be optimized to allow for exerting a necessary external torque with minimal effort.

关键词： Measurement Torque Service robots Shape Tools Programming Planning

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learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation

Learning Efficient Constraint Graph Sampling for Robotic Seq...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Joaquim Ortiz-Haro Valentin N. Hartmann Ozgur S. Oguz Marc Toussaint Machine Learning & Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Germany Max Planck Institute for Intelligent Systems Germany

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimization methods), can be highly inefficient and non-robust. We propose a novel framework to learn how to break the overall problem into smaller sequential sampling problems. Specifically, we leverage Monte-Carlo Tree Search to learn assignment orders for the variable-subsets, in order to minimize the computation time to generate feasible full samples. This strategy allows us to efficiently compute a set of diverse valid robot configurations for mode-switches within sequential manipulation tasks, which are waypoints for subsequent trajectory optimization or sampling-based motion planning algorithms. We show that the learning method quickly converges to the best sampling strategy for a given problem, and outperforms user-defined orderings or fully joint optimization, while providing a higher sample diversity. Video: https://***/mCNdvjTbHNI

关键词： Manifolds learning systems Costs Monte Carlo methods Conferences Optimization methods Planning

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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of machines in Manufacturing Industry

Systematic Literature Review on Industry Revolution 4.0 to P...

引用

Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： Sowmya P Sathish Kumar Ravichandran Rakshitha Department of Robotics and Artificial Intelligence NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India Department of Computer Science School of Engineering & Technology Christ University Bangalore Karnataka India Department of Artificial Intelligence and Machine Learning NMAM Institute of Technology-Affiliated to NITTE(Deemed to be University) Nitte Karnataka India

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance.

关键词： Manufacturing industries Productivity Production systems Systematics Companies Life estimation Market research

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