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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system

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arXiv 2023年

作者： Guan, Runwei Man, Ka Lok Chen, Feifan Yao, Shanliang Hu, Rongsheng Zhu, Xiaohui Smith, Jeremy Lim, Eng Gee Yue, Yutao Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3BX United Kingdom School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China XJTLU-JITRI Academy of Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Institute of Deep Perception Technology Jitri Wuxi214000 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 7ZX United Kingdom Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Natural language (NL) based vehicle retrieval is a task aiming to retrieve a vehicle that is most consistent with a given NL query from among all candidate vehicles. Because NL query can be easily obtained, such a task has a promising prospect in building an interactive intelligent traffic system (ITS). Current solutions mainly focus on extracting both text and image features and mapping them to the same latent space to compare the similarity. However, existing methods usually use dependency analysis or semantic role-labelling techniques to find keywords related to vehicle attributes. These techniques may require a lot of pre-processing and post-processing work, and also suffer from extracting the wrong keyword when the NL query is complex. To tackle these problems and simplify, we borrow the idea from named entity recognition (NER) and construct FindVehicle, a NER dataset in the traffic domain. It has 42.3k labelled NL descriptions of vehicle tracks, containing information such as the location, orientation, type and colour of the vehicle. FindVehicle also adopts both overlapping entities and fine-grained entities to meet further requirements. To verify its effectiveness, we propose a baseline NL-based vehicle retrieval model called VehicleFinder. Our experiment shows that by using text encoders pre-trained by FindVehicle, VehicleFinder achieves 87.7% precision and 89.4% recall when retrieving a target vehicle by text command on our homemade dataset based on UA-DETRAC [1]. From loading the command into VehicleFinder to identifying whether the target vehicle is consistent with the command, the time cost is 279.35 ms on one ARM v8.2 CPU and 93.72 ms on one RTX A4000 GPU, which is much faster than the Transformer-based system. The dataset is open-source via the link https://***/GuanRunwei/FindVehicle, and the implementation can be found via the link https://***/GuanRunwei/VehicleFinder-CTIM. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks

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arXiv 2023年

作者： Li, Yuhang Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer and Information Technology Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China State Key Laboratory of Rail Traffic Control and Safety the School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

The advantages of graph neural networks (GNNs) in leveraging the graph topology of wireless networks have drawn increasing attentions. This paper studies the GNN-based learning approach for the sum-rate maximization in multiple-user multiple-input single-output (MU-MISO) networks subject to the users’ individual data rate requirements and the power budget of the base station. By modeling the MU-MISO network as a graph, a GNN-based architecture named CRGAT is proposed to directly map the channel state information to the beamforming vectors. The attention-enabled aggregation and the residual-assisted combination are adopted to enhance the learning capability and avoid the oversmoothing issue. Furthermore, a novel activation function is proposed for the constraint due to the limited power budget at the base station. The CRGAT is trained in an unsupervised learning manner with two proposed loss functions. An evaluation method is proposed for the learning-based approach, based on which the effectiveness of the proposed CRGAT is validated in comparison with several convex optimization and learning based approaches. Numerical results are provided to reveal the advantages of the CRGAT including the millisecond-level response with limited optimality performance loss, the scalability to different number of users and power budgets, and the adaptability to different system settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

Multi-objective vehicle path planning method for recycling process of used electrical and electronic products

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Multi-objective vehicle path planning method for recycling p...

2020 IEEE International Symposium on Product Compliance engineering-Asia, ISPCE-CN 2020

作者： Yang, Cuili Wu, Zhanhong Han, Honggui Bi, Xiaowen Beijing University of Technology Faculty of Information Technology Beijing China City University of Hong Kong Department of Electrical Engineering Hong Kong Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligence System Eng. Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Department of Electrical Engineering City University of Hong Kong. Hong Kong

ISBN: (纸本)9781665414616

During the recycling process of used electrical and electronic products from recycling places to sorting center, the vehicle path planning is an urgent problem to be solved which needs to minimize the number of vehicles and vehicle transportation distance simultaneously. To solve this problem, a multi-objective optimization algorithm is proposed which combines the genetic algorithm (GA) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Firstly, the vehicle path planning model of the sorting center is established according to characteristics of product transportation process. Secondly, the GA is used to produce some vehicle path planning schemes. Then, the MOEA/D is introduced to deal with the trade-off relationship between two objectives. Simulation results demonstrate that the proposed algorithm is able to generate a set of non-dominated solutions. © 2020 IEEE.

关键词： Genetic algorithms

Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA

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arXiv 2024年

作者： Wang, Chunjie Zhang, Xuhui Xing, Huijun Xue, Liang Wang, Shuqiang Shen, Yanyan Yang, Bo Guan, Xinping Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Intelligent reflecting surface (IRS) and rate-splitting multiple access (RSMA) technologies are at the forefront of enhancing spectrum and energy efficiency in the next generation multi-antenna communication systems. This paper explores a RSMA system with multiple IRSs, and proposes two purpose-driven scheduling schemes, i.e., the exhaustive IRS-aided (EIA) and opportunistic IRS-aided (OIA) schemes. The aim is to optimize the system weighted energy efficiency (EE) under the above two schemes, respectively. Specifically, the Dinkelbach, branch and bound, successive convex approximation, and the semidefinite relaxation methods are exploited within the alternating optimization framework to obtain effective solutions to the considered problems. The numerical findings indicate that the EIA scheme exhibits better performance compared to the OIA scheme in diverse scenarios when considering the weighted EE, and the proposed algorithm demonstrates superior performance in comparison to the baseline algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Utility coupling promotes cooperation in multiplayer snowdrift games on interdependent simplicial networks

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The European Physical Journal Special Topics 2023年第4期233卷 831-842页

作者： Wang, Juan Guo, Shiqiang Xia, Chengyi Perc, Matjaž Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin People’s Republic of China School of Artificial Intelligence Tiangong University Tianjin People’s Republic of China Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Alma Mater Europaea Maribor Slovenia Complexity Science Hub Vienna Vienna Austria Department of Physics Kyung Hee University Seoul Republic of Korea

Real-world social networks often contain not only one-to-one pairwise interactions, but also multi-person group interactions involving more than two individuals. To reveal the important role of such group interactions (i.e., higher order topology) for the evolution of cooperation, we here use interdependent simplicial network topologies, where the evolutionary interactions, governed by the snowdrift game, are thus explored beyond pairwise links. We also consider utility coupling between two network layers in an uncorrelated interdependence. Systematic Monte Carlo simulations lead us to two main conclusions. First, we find that the level of cooperation can be elevated by increasing the utility coupling value. And second, cooperation tends to decline when we increase the number of 2-simplex interactions. However, despite the later result, the overall cooperation level on both interdependent networks is still higher than that on any isolated network. We hope that these results will inspire future research on cooperation among unrelated individuals in higher order networks.

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An IGD+ Performance Indicator Based Particle Swarm Optimizer For Multi-objective Optimization

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An IGD+ Performance Indicator Based Particle Swarm Optimizer...

第33届中国控制与决策会议

作者： Fei Li Shijian Dong Yuanqu Liu Zhengkun Shang School of Electrical and Information Engineering Anhui Unviersity of Technology AnHui Province Key Laboratory of Special Heavy Load Robot Anhui Unviersity of Technology Ma'anshan University School of Information and Control Engineering China University of Mining and Technology Department of Automation North China Electric Power University

Particle swarm optimizer（PSO） is suitable for solving multi-objective optimization problems（MOPs）.However,there are two main issues for any multi-objective particle swarm optimizers（MOPSOs）.The first issue is how to balance the convergence and *** second issue is how to enhance the exploitation and exploration during the evolutionary *** order to address these issues,an modified inverted generational distance（IGD） performance indicator based PSO（IGD-MOPSO） is *** external archive updating strategies based on the IGD indicator and the objective space decomposition method are proposed to select the evenly distributed non-dominated *** leader updated strategy of each particle is based on the IGD indicator value which is associated to the corresponding reference *** genetic operator is embedded into the evolutionary procedure to reset the position in order to help the particle jump out of the local *** have conducted the simulation on some related benchmark test *** experimental results have indicated that the proposed algorithm is competitive with some related algorithms.

关键词： IGD+ indicator Particle swarm optimization The external archive updated strategy Objective space decomposition Multi-objective optimization problems

Implementation of a Robust Electric Brake Actuator Design Based on H-infinity control Theory

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Periodica Polytechnica Transportation engineering 2019年第3期47卷 178-185页

作者： Péter, Gábor Max, György Kiss, Bálint Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P.O.B. 91 BudapestH-1521 Hungary

This paper deals with the robust control system design and implementation of a brake actuator for a smart car. To deliver robust performance, an H∞ controller had been chosen for the task. This allows excellent disturbance rejection while requiring low computational needs. In order to realize the controller a nominal model of the system has been identified, then, the parameter uncertainties were taken into account to find the stabilizing controller. The brake system consists of a low level H∞ controller sustaining robustness, a mid-level serial compensator for effective setpoint tracking and a high level supervisory control logic to deliver a reliable system. The implementation was tested and verified on a test bench using rapid prototyping tools and HIL methods. © 2019 Budapest University of technology and Economics. All rights reserved.

关键词： Robust control