Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviat...
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In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primi...
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In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint *** DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion *** addition,motions are categorized into different goals and *** is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning *** experiment test on the Baxter robot verifies the effectiveness of the proposed method.
Point cloud completion concentrates on completing geometric and topological shapes from incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the generation of high-quality point clouds ...
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Point cloud completion concentrates on completing geometric and topological shapes from incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the generation of high-quality point clouds without predicting structured and topological information of the complete shapes and introducing noisy points. To effectively address the challenges posed by missing topology and noisy points, we introduce SPOFormer, a novel topology-aware model that utilizes surface-projection optimization in a progressive growth manner. SPOFormer consists of three distinct steps for completing the missing topology: (1) Missing Keypoints Prediction. A topology-aware transformer auto-encoder is integrated for missing keypoint prediction. (2) Skeleton Generation. The skeleton generation module produces a new type of representation named skeletons with the aid of keypoints predicted by topology-aware transformer auto-encoder and the partial input. (3) Progressively Growth. We design a progressive growth module to predict final output under Multi-scale Supervision and Surface-projection Optimization. Surface-projection Optimization is firstly devised for point cloud completion, aiming to enforce the generated points to align with the underlying object surface. Experimentally, SPOFormer model achieves an impressive Chamfer Distance-$\ell _{1}$ (CD) score of 8.11 on PCN dataset. Furthermore, it attains average CD-$\ell _{2}$ scores of 1.13, 1.14, and 1.70 on ShapeNet-55, ShapeNet-34, and ShapeNet-Unseen21 datasets, respectively. Additionally, the model achieves a Maximum Mean Discrepancy (MMD) of 0.523 on the real-world KITTI dataset. These outstanding qualitative and quantitative performances surpass previous approaches by a significant margin, firmly establishing new state-of-the-art performance across various benchmark datasets. Our code is available at https://***/kiddoray/SPOFormer IEEE
In everyday interactions between humans and computers, recognizing hand gestures plays a crucial role as it offers a natural and easy way to control and communicate with various applications. This article delves into ...
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Owing to the ppb-level detection standard toward the toxic and harmful gas,the detection of trace gases has become an important subject in the field of indoor environment ***,the traditional resistive gas sensors hard...
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Owing to the ppb-level detection standard toward the toxic and harmful gas,the detection of trace gases has become an important subject in the field of indoor environment ***,the traditional resistive gas sensors hardly meet the requirement due to the weak signal generated by trace gas molecules that are difficult to ***,a visible-light-assisted Pd/TiO_(2)gas sensor is proposed to endow the effective detection of trace formaldehyde(HCHO)gas without heating *** from the enhanced photocatalytic properties of TiO_(2)by Pd decoration,the visible-light-assisted Pd/TiO_(2)gas sensor can detect the HCHO gas as low as80×10^(–9)at room *** successful preparation of nanoscale TiO_(2)sensing layer is facilitated by the ultrathin carbon nanotube interdigital electrode in the gas sensor,which avoids the discontinuity of the sensing layer caused by the excessive thickness of the traditional metal *** addition,the whole preparation process of the Pd/TiO_(2)gas sensor with carbon nanotube electrodes is compatible with mainstream CMOS fabrication technology,which is expected to realize the batch fabrication and micro-integrated application of gas *** is expected that our work can provide a new strategy for the batch preparation of high-performance trace HCHO gas sensors and their future applications in portable electronic devices such as smartphones.
Wireless Sensor Networks (WSNs) face critical energy efficiency challenges due to resource limitations, especially in extending network lifetime. This paper presents a reinforcement learning-based solution combining L...
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The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a...
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The prevalence of social media and mobile computing has led to intensive user engagement in the emergent Cyber-Physical-Social-Thinking(CPST)***,the easy access,the lack of governance,and excessive use has generated a raft of new behaviors within CPST,which affects users’physical,social,and mental *** this paper,we conceive the Cyber-Syndrome concept to denote the collection of cyber disorders due to excessive or problematic Cyberspace interactions based on CPST *** we characterize the Cyber-Syndrome concept in terms of Maslow’s theory of Needs,from which we establish an in-depth theoretical understanding of Cyber-Syndrome from its etiology,formation,symptoms,and ***,we propose an entropy-based Cyber-Syndrome control mechanism for its computation and *** goal of this study is to give new insights into this rising phenomenon and offer guidance for further research and development.
Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependenc...
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In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swar...
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This study aimed to elucidate the relationship between cumulative family risk and adolescents’ depressive symptoms, and to investigate the mediating role of interpersonal trust through a longitudinal design. Particip...
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This study aimed to elucidate the relationship between cumulative family risk and adolescents’ depressive symptoms, and to investigate the mediating role of interpersonal trust through a longitudinal design. Participants were selected from the China Family Panel Studies (CFPS) project, with data collected in 2016 (T1) and 2018 (T2). A total of 785 adolescents participated in the study and completed questionnaires. (1) The cross-lagged model revealed that cumulative family risk positively predicted adolescents’ depressive symptoms at both T1 and T2. (2) Cumulative family risk at T1 negatively predicted interpersonal trust at T2, which in turn negatively predicted depressive symptoms at T2. Thus, interpersonal trust at T2 served as a longitudinal mediator in the effect of cumulative family risk at T1 on depressive symptoms at T2. These results deepen our understanding of the mechanisms by which cumulative family risk influences depressive symptoms and offer strategies for mitigating depressive symptoms in adolescents.
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