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arXiv 2023年

作者： Schumacher, Pierre Geijtenbeek, Thomas Caggiano, Vittorio Kumar, Vikash Schmitt, Syn Martius, Georg Haeufle, Daniel F.B. Max-Planck Institute for Intelligent Systems Tübingen Germany Hertie Institute for Clinical Brain Research Center for Integrative Neuroscience Tübingen Germany Goatstream Netherlands Meta AI New York United States Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany Institute of Computer Engineering Heidelberg University Germany

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multiobjective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning (RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Hydrone: Modeling of a Hybrid Unmanned Aerial Underwater Vehicle Considering Trans-Media Dynamics

SSRN

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SSRN 2024年

作者： de Oliveira Evald, Paulo Jefferson Dias Brião, Stephanie Loi Aoki, Vivian Misaki da Silva, César Bastos dos Santos Cardoso, Dayana Pinheiro, Pedro Miranda de Oliveira, Felipe Gomes André Barbosa Cunha, Mauro Jorge Drews, Paulo Lilles Intelligent Systems and Control Group Federal University of Pelotas RS Pelotas Brazil Intelligent Robotics and Automation Group Federal University of Rio Grande RS Rio Grande Brazil Department of Computer Engineering and Industrial Automation State University of Campinas SP Campinas Brazil Federal Institute of Education Science and Technology in Rio Grande do Sul RS Pelotas Brazil Institute of Exact Sciences and Technology Federal University of Amazonas Itacoatiara AM Amazonas Brazil Brazil

Research in mobile robotics is growing into applications for difficult-to-access environments, such as in rescue and transport missions. Furthermore, autonomous vehicles can perform data collection in complex approach areas. Therefore, the study of hybrid unmanned vehicles is relevant for several areas, since these vehicles can act in different domains. The developed vehicle, called Hydrone, is designed in a quadrotor-type structure and equipped with a set of aerial and underwater propellers, which are fixed and independent between them. In this sense, this work presents the mathematical modeling of an unmanned hybrid vehicle, which can navigate underwater and aerial environments. Furthermore, the modeling considers the ground effect due to the influence on the vehicle’s trajectory change and the dynamics of the transition zone between water-air and air-water. These dynamics are important to evaluate the controller’s performance. Numerical simulations consider both transitions (water-air and air-water) and they are performed to discuss the influence of acting forces on the vehicle stability and position-tracking performance using a classical control system. © 2024, The Authors. All rights reserved.

关键词： Hybrid vehicles

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Hyper-parameter optimization on viola jones algorithm for gesture recognition

Hyper-parameter optimization on viola jones algorithm for ge...

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International Conference on Modelling, Simulation and intelligent Computing, MoSICom 2020

作者： Pande, Aditya Rout, B.K. Das, Sangram K. Department of Mechanical Engineering Center for Robotics and Intelligent Systems Birla Institute of Technology and Science Pilani Campus PilaniRajasthan India

ISBN: (纸本)9789811547744

The problem of features, objects, gestures, and face detection has been tackled using a numerous vision-based algorithms available in literature. Each of these algorithms requires a set of hyper-parameters, which need to be set on the basis of trial and error such that the results provide best performance to a situation. Mostly, researchers use trial and error approach to satisfactory result and solve the above problems. In this work, an approach has been suggested to determine an optimum set of hyper-parameters, which will provide a starting point for anyone using Viola Jones algorithm for hand gesture recognition or similar endeavors. This will reduce the time spent in searching for the best combination of hyper-parameters. © Springer Nature Singapore Pte Ltd 2020.

关键词： computer vision

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells

Application of Deep Learning Method to Estimate Bottomhole P...

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IeeE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.

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BYTECounter: Improving Vehicle Turning-Movement Counting

BYTECounter: Improving Vehicle Turning-Movement Counting

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International Conference on intelligent Transportation

作者： Mateus Líbano Monteiro Alexandre Bernardino H. Sofia Pinto Computer Science: Artificial Intelligence and Cybersecurity from Instituto Superior Técnico Universidade de Lisboa Portugal Department of Electrical and Computer Engineering Institute for and Systems and Robotics of Instituto Superior Técnico Universidade de Lisboa Portugal INESC-ID and Department of Computer Science and Artificial Intelligence Instituto Superior Técnico Universidade de Lisboa Portugal

This paper proposes a methodology to enhance the accuracy of vehicle turning-movement counting in video footage. The methodology uses a three-component system comprising object detection, object tracking, and object turning-movement counting. To improve vehicle detection, a custom vehicle neural network called YOLOv7-vehicles-0.1 was trained using a unique vehicle dataset comprising COCO, KITTI, and OpenImages datasets. Additionally, a novel tracker named BYTECounter was developed, using two virtual drawing objects, Regions and Lines, to count vehicles' turning movements based on trajectory information. The performance of our system was evaluated using the UA-DETRAC dataset, achieving an overall Movement Counting Accuracy (MCAcc) of 93.5% with a processing rate of 82.86 frames per second. This paper demonstrates that BYTECounter outperforms the state-of-the-art tracker, BYTE, and highlights the superiority, in terms of counting metrics, of YOLOv7-vehicles-0.1 over YOLOv7 models trained on the COCO dataset.

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Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics

arXiv

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arXiv 2021年

作者： Wang, Ruigang Koelwijn, Patrick J.W. Manchester, Ian R. Tóth, Roland Australian Centre for Field Robotics Sydney Institute for Robotics and Intelligent Systems University of Sydney SydneyNSW2006 Australia Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set. This VCCM based nonlinear stabilization and performance synthesis approach, which is similar to linear parameter-varying (LPV) control approaches, allows to achieve exact guarantees of exponential stability and L2-gain performance on nonlinear systems with respect to all trajectories from the predetermined set, which is not the case with the conventional LPV methods. Simulation and experimental studies conducted in both fully- and under-actuated operating modes of the CMG show effectiveness of this approach compared to standard LPV control methods. © 2021, CC BY.

关键词： State feedback

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Preliminaries to artificial consciousness: a multidimensional heuristic approach.

arXiv

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arXiv 2024年

作者： Evers, K. Farisco, M. Chatila, R. Earp, B.D. Freire, I.T. Hamker, F. Nemeth, E. Verschure, P.F.M.J. Khamassi, M. Centre for Research Ethics and Bioethics Uppsala University Uppsala Sweden Ariano Irpino Italy Institute of Intelligent Systems and Robotics Sorbonne University CNRS Paris France Uehiro Centre for Practical Ethics University of Oxford Oxford United Kingdom Centre for Biomedical Ethics National University of Singapore Singapore Artificial Intelligence Computer Science Chemnitz University of Technology Germany Alicante Institute of Neuroscience Department of Health Psychology Universidad Miguel Hernandez Spain

The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication. We argue that this model provides a balanced approach to artificial consciousness research by avoiding binary thinking (e.g., conscious vs. non-conscious) and offering a structured basis for testable hypotheses. To illustrate its utility, we focus on "awareness" as a case study, demonstrating how specific dimensions of consciousness can be pragmatically analyzed and targeted for potential artificial instantiation. By breaking down the conceptual intricacies of consciousness and aligning them with practical research goals, this paper lays the groundwork for a robust strategy to advance the scientific and technical understanding of artificial consciousness. © 2024, CC BY.

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Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections

Curriculum Proximal Policy Optimization with Stage-Decaying ...

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International Conference on intelligent Transportation

作者： Zengqi Peng Xiao Zhou Yubin Wang Lei Zheng Ming Liu Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in such scenarios, this paper proposes a curriculum proximal policy optimization (CPPO) framework with stage-decaying clipping. By adjusting the clipping parameter during different stages of training through proximal policy optimization (PPO), the vehicle can first rapidly search for an approximate optimal policy or its neighborhood with a large parameter, and then converges to the optimal policy with a small one. Particularly, the stage-based curriculum learning technology is incorporated into the proposed framework to improve the generalization performance and further accelerate the training process. Moreover, the reward function is specially designed in view of different curriculum settings. A series of comparative experiments are conducted in intersection-crossing scenarios with bi-lane carriageways to verify the effectiveness of the proposed CPPO method. The results show that the proposed approach demonstrates better adaptiveness to different dynamic and complex environments, as well as faster training speed over baseline methods.

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Interplay Between Neural Computational Energy and Multimodal Processing in Robot-Robot Interaction

Interplay Between Neural Computational Energy and Multimodal...

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IeeE International Conference on Development and Learning, ICDL

作者： Murat Kirtay Verena V. Hafner Minoru Asada Erhan Oztop department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Department of Computer Science Adaptive Systems Group Humboldt-Universität zu Berlin Berlin Germany Research Cluster of Excellence Science of Intelligence Berlin Symbiotic Intelligent Systems Research Center Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan International Professional University of Technology in Osaka Osaka Japan Ozyegin University Istanbul Turkey

Multimodal learning is an active research area that is gaining importance in human-robot interaction. Despite the obvious benefit of levering multiple sensors for perceiving the world, its neural computational cost has not been addressed in robotics, especially in Robot-Robot Interaction (RRI). This study addresses the role of computational cost in multimodal processing by considering robot-robot interaction in a sequential multimodal memory recall task. In this setting, the learner (Nao) robot receives auditory-only, visual-only, or audio-visual information from an instructor (Pepper) robot and the environment regarding previously learned memory items. The goal of the learner robot is to perform the interactive task with as low as possible neural computational cost. The learner robot has two cognitive modules: a multimodal auto-associative network that stands for the perceptual-cognitive processing of the robot and an internal reward mechanism that monitors the changes in neural energy incurred for two consecutive steps by the processing of the attended stimuli. The reward computed is used to build an action policy for minimizing the neural energy consumption over the sequential memory recall task. The experimental results show that having access to both auditory and visual information is beneficial not only for better memory recall but also for minimizing the cost of neural computation.

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Triboelectric Nanogenerators and Hybridized systems for Enabling Next-Generation IoT Applications

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Research 2021年第1期2021卷 392-421页

作者： Qiongfeng Shi Zhongda Sun Zixuan Zhang Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3SingaporeSingapore 117583 Center for Intelligent Sensors and MEMS National University of Singapore4 Engineering Drive 3SingaporeSingapore 117583 Smart Systems Institute National University of Singapore3 Research LinkSingaporeSingapore 117602 NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingaporeSingapore 117456

In the past few years,triboelectric nanogenerator-based(TENG-based)hybrid generators and systems have experienced a widespread and flourishing development,ranging among almost every aspect of our lives,e.g.,from industry to consumer,outdoor to indoor,and wearable to implantable *** TENG technology has been extensively investigated for mechanical energy harvesting,most developed TENGs still have limitations of small output current,unstable power generation,and low energy utilization rate of multisource *** harvest the ubiquitous/coexisted energy forms including mechanical,thermal,and solar energy simultaneously,a promising direction is to integrate TENG with other transducing mechanisms,e.g.,electromagnetic generator,piezoelectric nanogenerator,pyroelectric nanogenerator,thermoelectric generator,and solar cell,forming the hybrid generator for synergetic single-source and multisource energy *** resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations,toward achieving a maximum,reliable,and stable output ***,in this review,we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems,in the aspects of operation principles,structure designs,optimization strategies,power management,and system *** recent progress of TENG-based hybrid generators and hybridized systems for the outdoor,indoor,wearable,and implantable applications is also ***,we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring,human activity sensation,human-machine interaction,smart home,healthcare,wearables,implants,robotics,Internet of things(IoT),and many other fields.

关键词： IoT generator harvesting

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