Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning...
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Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.
In recent years, underwater fine three-dimensional reconstruction technology has emerged as a focal point in research, exhibiting extensive potential applications, particularly in underwater archaeology and resource e...
In recent years, underwater fine three-dimensional reconstruction technology has emerged as a focal point in research, exhibiting extensive potential applications, particularly in underwater archaeology and resource exploration [1, 2].
Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the...
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Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.
In recent years, robot teleoperation systems have been widely used in assembly tasks due to their ability to combine human control with robotic execution, addressing limitations in precision, perception, and intellige...
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In recent years, robot teleoperation systems have been widely used in assembly tasks due to their ability to combine human control with robotic execution, addressing limitations in precision, perception, and intelligence faced by fully automated robots. However, robot teleoperation for assembly tasks still encounters several challenges. In complex and unstructured environments, telerobots must regulate the posture of their manipulators to avoid obstacles and singularities, ensuring the safety of human operators [1].
After eons of evolution,biological systems have developed excellent perfor-mance in terms of sensing,intelligence,and actuation—the three primary robotics elements that are closely re-lated to the overall performance...
After eons of evolution,biological systems have developed excellent perfor-mance in terms of sensing,intelligence,and actuation—the three primary robotics elements that are closely re-lated to the overall performance of robot *** example,the buccal pits of some snakes,as the necessary sensors for accurate predation,have a high-temperature sensitivity of 0.003 K[1];the biological brain can exhibit an excellent capacity for efficient learning and multitasking operations[2];and froghopper insects can leap to a height of more than 100 times their body length[3].Most of these biological system per-formances are difficult to realize with tra-ditional electromechanical systems.
Nonnegative CANDECOMP/PARAFAC(NCP) tensor decomposition is a powerful tool for multiway signal processing. The alternating direction method of multipliers(ADMM) optimization algorithm has become increasingly popul...
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Nonnegative CANDECOMP/PARAFAC(NCP) tensor decomposition is a powerful tool for multiway signal processing. The alternating direction method of multipliers(ADMM) optimization algorithm has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However,the ADMM-based NCP algorithm suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers(ADPMM) that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named *** experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.
Saccharomyces Cerevisiae (S. cerevisiae) is a fermenting strain that commonly used in the baking and brewing industries and also used as a probiotic for the prevention and treatment of various diarrhea and related dis...
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Multi-leaf journal foil bearing (MLJFB) structures are complex, making performance prediction challenging, and the accurate prediction of gas film thickness and pressure distributions is crucial. In this study, comsol...
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A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive ***,it still has a gap between scientific research and real-life *** studies mainly focused on EMG...
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A long history has passed since electromyography(EMG)signals have been explored in human-centered robots for intuitive ***,it still has a gap between scientific research and real-life *** studies mainly focused on EMG decoding algorithms,leaving a dynamic relationship between the human,robot,and uncertain environment in real-life scenarios seldomly *** fill this gap,this paper presents a comprehensive review of EMG-based techniques in human-robot-environment interaction(HREI)*** general processing framework is summarized,and three interaction paradigms,including direct control,sensory feedback,and partial autonomous control,are ***-based intention decoding is treated as a module of the proposed *** key issues involving precision,stability,user attention,compliance,and environmental awareness in this field are *** important directions,including EMG decomposition,robust algorithms,HREI dataset,proprioception feedback,reinforcement learning,and embodied intelligence,are proposed to pave the way for future *** the best of what we know,this is the first time that a review of EMG-based methods in the HREI system is *** provides a novel and broader perspective to improve the practicability of current myoelectric interaction systems,in which factors in human-robot interaction,robot-environment interaction,and state perception by human sensations are considered,which has never been done by previous studies.
Under the influence of unstructured environmental constraints, the spatial morphological changes in slender soft robots are primarily determined by the robot's force situation. Therefore, this paper focused on the...
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