Pub Date : 2024-11-04DOI: 10.1109/TCDS.2024.3489773
Lipeng Wang;Xiaochen Wang;Junjun Huang;Mengjie Liu
A task and motion planning method for service robot arm based on 3-D voxel-semantic maps is proposed, which can realize virtual environment mapping, manipulator planning, and grasping tasks in unknown environments. First of all, a complete point cloud scene is obtained and spliced. Mask region-based convolutional neural network (RCNN) network is used to complete object detection and instance segmentation. A voxel-semantic hybrid map composed of 3-D point cloud, semantic information, and 3-D computer aided design (CAD) model is constructed. Second, an improved A* algorithm is proposed to plan the optimal path of robot arm end-effector. The Bezier curve interpolation is introduced to obtain the smooth trajectory. Third, the grasping poses of the robot gripper corresponding to different geometries are explored. Semantic-driven spatial task planning is achieved by decomposing robotic arm pick and place tasks. Finally, the effectiveness and rapidity of the proposed algorithm are verified in virtual space and real physical space, respectively.
{"title":"Task and Motion Planning of Service Robot Arm in Unknown Environment Based on Virtual Voxel-Semantic Space","authors":"Lipeng Wang;Xiaochen Wang;Junjun Huang;Mengjie Liu","doi":"10.1109/TCDS.2024.3489773","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3489773","url":null,"abstract":"A task and motion planning method for service robot arm based on 3-D voxel-semantic maps is proposed, which can realize virtual environment mapping, manipulator planning, and grasping tasks in unknown environments. First of all, a complete point cloud scene is obtained and spliced. Mask region-based convolutional neural network (RCNN) network is used to complete object detection and instance segmentation. A voxel-semantic hybrid map composed of 3-D point cloud, semantic information, and 3-D computer aided design (CAD) model is constructed. Second, an improved A* algorithm is proposed to plan the optimal path of robot arm end-effector. The Bezier curve interpolation is introduced to obtain the smooth trajectory. Third, the grasping poses of the robot gripper corresponding to different geometries are explored. Semantic-driven spatial task planning is achieved by decomposing robotic arm pick and place tasks. Finally, the effectiveness and rapidity of the proposed algorithm are verified in virtual space and real physical space, respectively.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"564-576"},"PeriodicalIF":5.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1109/TCDS.2024.3489357
Kai Shu;Le Wu;Yuchang Zhao;Aiping Liu;Ruobing Qian;Xun Chen
Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-based seizure prediction methods. However, existing DA approaches are just the linear transformations of original data and cannot explore the feature space to increase diversity effectively. Therefore, we propose a novel diffusion-based DA method called DiffEEG. DiffEEG can fully explore data distribution and generate samples with high diversity, offering extra information to classifiers. It involves two processes: the diffusion process and the denoised process. In the diffusion process, the model incrementally adds noise with different scales to EEG input and converts it into random noise. In this way, the representation of data can be learned. In the denoised process, the model utilizes learned knowledge to sample synthetic data from random noise input by gradually removing noise. The randomness of input noise and the precise representation enable the synthetic samples to possess diversity while ensuring the consistency of feature space. We compared DiffEEG with original, down-sampling, sliding windows and recombination methods, and integrated them into five representative classifiers. The experiments demonstrate the effectiveness and generality of our method. With the contribution of DiffEEG, the multiscale CNN achieves state-of-the-art performance, with an average sensitivity, FPR, AUC of 95.4%, 0.051/h, 0.932 on the CHB-MIT database and 93.6%, 0.121/h, 0.822 on the Kaggle database.
{"title":"Data Augmentation for Seizure Prediction With Generative Diffusion Model","authors":"Kai Shu;Le Wu;Yuchang Zhao;Aiping Liu;Ruobing Qian;Xun Chen","doi":"10.1109/TCDS.2024.3489357","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3489357","url":null,"abstract":"Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-based seizure prediction methods. However, existing DA approaches are just the linear transformations of original data and cannot explore the feature space to increase diversity effectively. Therefore, we propose a novel diffusion-based DA method called DiffEEG. DiffEEG can fully explore data distribution and generate samples with high diversity, offering extra information to classifiers. It involves two processes: the diffusion process and the denoised process. In the diffusion process, the model incrementally adds noise with different scales to EEG input and converts it into random noise. In this way, the representation of data can be learned. In the denoised process, the model utilizes learned knowledge to sample synthetic data from random noise input by gradually removing noise. The randomness of input noise and the precise representation enable the synthetic samples to possess diversity while ensuring the consistency of feature space. We compared DiffEEG with original, down-sampling, sliding windows and recombination methods, and integrated them into five representative classifiers. The experiments demonstrate the effectiveness and generality of our method. With the contribution of DiffEEG, the multiscale CNN achieves state-of-the-art performance, with an average sensitivity, FPR, AUC of 95.4%, 0.051/h, 0.932 on the CHB-MIT database and 93.6%, 0.121/h, 0.822 on the Kaggle database.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"577-591"},"PeriodicalIF":5.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1109/TCDS.2024.3485482
Youness Boutyour;Abdellah Idrissi
In this article, we introduce a novel framework that combines constraint logic programming (CLP) with deep reinforcement learning (DRL) to create adaptive environments for continual learning. We focus on two challenging domains: Sudoku puzzles and scheduling problems, where environment complexity evolves based on the agent's performance. By integrating CLP, we dynamically adjust problem difficulty in response to the agent's learning trajectory, ensuring a progressively challenging environment that fosters enhanced problem-solving skills. Empirical results across 500 000 episodes show substantial improvements in solve rates, increasing from 6% to 86% for sudoku puzzles and 7% to 79% for scheduling problems, alongside significant reductions in the average steps required to solve each problem. The proposed adaptive environment generation demonstrates the potential of CLP in advancing RL agents’ continual learning capabilities by dynamically regulating complexity, thus improving their adaptability and learning efficiency. This framework contributes to the broader fields of reinforcement learning and procedural content generation by introducing an innovative approach to continual adaptation in complex environments.
{"title":"Adaptive Environment Generation for Continual Learning: Integrating Constraint Logic Programming With Deep Reinforcement Learning","authors":"Youness Boutyour;Abdellah Idrissi","doi":"10.1109/TCDS.2024.3485482","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3485482","url":null,"abstract":"In this article, we introduce a novel framework that combines constraint logic programming (CLP) with deep reinforcement learning (DRL) to create adaptive environments for continual learning. We focus on two challenging domains: Sudoku puzzles and scheduling problems, where environment complexity evolves based on the agent's performance. By integrating CLP, we dynamically adjust problem difficulty in response to the agent's learning trajectory, ensuring a progressively challenging environment that fosters enhanced problem-solving skills. Empirical results across 500 000 episodes show substantial improvements in solve rates, increasing from 6% to 86% for sudoku puzzles and 7% to 79% for scheduling problems, alongside significant reductions in the average steps required to solve each problem. The proposed adaptive environment generation demonstrates the potential of CLP in advancing RL agents’ continual learning capabilities by dynamically regulating complexity, thus improving their adaptability and learning efficiency. This framework contributes to the broader fields of reinforcement learning and procedural content generation by introducing an innovative approach to continual adaptation in complex environments.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"540-553"},"PeriodicalIF":5.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TCDS.2024.3485886
Yu-Jia Chen;Wei Chen;Sai Qian Zhang;Hai-Yan Huang;H.T. Kung
Radio-based human sensing has attracted substantial research attention due to its wide range of applications, including e-healthcare monitoring, indoor security, and industrial surveillance. However, most existing studies rely on fixed receivers to capture wireless signal perturbations. This article introduces UH-Sense, the first human sensing system using an unmanned aerial vehicle (UAV) equipped with an omnidirectional antenna to measure signal strength from surrounding WiFi access points (APs). UH-Sense addresses the challenge of multisource UAV-induced noise with a novel data-driven learning-based approach that denoises corrupted data without prior knowledge of noise characteristics. Furthermore, we develop a localization model based on radio tomography imaging (RTI) that localizes humans without collecting the fingerprint database. We demonstrate that UH-Sense is readily deployable on commodity platforms and evaluate its performance in different real-world environments including irregular AP deployment and nonline-of-sight (NLOS) scenarios. Experimental results show that UH-Sense achieves a high detection performance with an average F1 score of 0.93 and yields similar or even better localization performance than that of using clean data (i.e., data collected at a fixed receiver), which has not been achieved by any of the state-of-the-art denoising methods.
{"title":"A Task-Oriented Deep Learning Approach for Human Localization","authors":"Yu-Jia Chen;Wei Chen;Sai Qian Zhang;Hai-Yan Huang;H.T. Kung","doi":"10.1109/TCDS.2024.3485886","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3485886","url":null,"abstract":"Radio-based human sensing has attracted substantial research attention due to its wide range of applications, including e-healthcare monitoring, indoor security, and industrial surveillance. However, most existing studies rely on fixed receivers to capture wireless signal perturbations. This article introduces UH-Sense, the first human sensing system using an unmanned aerial vehicle (UAV) equipped with an omnidirectional antenna to measure signal strength from surrounding WiFi access points (APs). UH-Sense addresses the challenge of multisource UAV-induced noise with a novel data-driven learning-based approach that denoises corrupted data without prior knowledge of noise characteristics. Furthermore, we develop a localization model based on radio tomography imaging (RTI) that localizes humans without collecting the fingerprint database. We demonstrate that UH-Sense is readily deployable on commodity platforms and evaluate its performance in different real-world environments including irregular AP deployment and nonline-of-sight (NLOS) scenarios. Experimental results show that UH-Sense achieves a high detection performance with an average F1 score of 0.93 and yields similar or even better localization performance than that of using clean data (i.e., data collected at a fixed receiver), which has not been achieved by any of the state-of-the-art denoising methods.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"525-539"},"PeriodicalIF":5.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1109/TCDS.2024.3485078
Zhiwei Song;Xiang Zhang;Shuhang Chen;Jieyuan Tan;Yiwen Wang
Reinforcement learning (RL)-based brain–machine interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multistep BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this article, we propose a kernel-based actor–critic learning framework for multistep BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial prefrontal cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multistep BC tasks.
{"title":"Kernel-Based Actor–Critic Learning Framework for Autonomous Brain Control on Trajectory","authors":"Zhiwei Song;Xiang Zhang;Shuhang Chen;Jieyuan Tan;Yiwen Wang","doi":"10.1109/TCDS.2024.3485078","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3485078","url":null,"abstract":"Reinforcement learning (RL)-based brain–machine interfaces (BMIs) hold promise for restoring motor functions in paralyzed individuals. These interfaces interpret neural activity to control external devices through trial-and-error. In brain control (BC) tasks, subjects control the device continuously moving in space by imagining their own limb movement, in which the subject can change direction at any position before reaching the target. Such multistep BC tasks span a large space both in neural state and over a sequence of movements. However, conventional RL decoders face challenges in efficient exploration and limited guidance from delayed rewards. In this article, we propose a kernel-based actor–critic learning framework for multistep BC tasks. Our framework integrates continuous trajectory control (actor) and internal continuous state value estimation (critic) from medial prefrontal cortex (mPFC) activity. We evaluate our algorithm's performance in a BC three-lever discrimination task using data from two rats, comparing it to a kernel RL decoder with internal binary rewards and delayed external rewards. Experimental results show that our approach achieves faster convergence, shorter target-acquisition time, and shorter distances to targets. These findings highlight the potential of our algorithm for clinical applications in multistep BC tasks.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"554-563"},"PeriodicalIF":5.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1109/TCDS.2024.3480854
Huayang Wu;Chengzhi Zhu;Long Cheng;Chenguang Yang;Yanan Li
Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this article to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on these estimations for physical human–robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. To assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort.
{"title":"Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human–Robot Interaction","authors":"Huayang Wu;Chengzhi Zhu;Long Cheng;Chenguang Yang;Yanan Li","doi":"10.1109/TCDS.2024.3480854","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3480854","url":null,"abstract":"Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this article to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on these estimations for physical human–robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. To assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"510-524"},"PeriodicalIF":5.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TCDS.2024.3458488
Guoqi Li;Emre Neftci;Rong Xiao;Pablo Lanillos;Kaushik Roy
{"title":"Guest Editorial: Special Issue on Advancing Machine Intelligence With Neuromorphic Computing","authors":"Guoqi Li;Emre Neftci;Rong Xiao;Pablo Lanillos;Kaushik Roy","doi":"10.1109/TCDS.2024.3458488","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3458488","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"1623-1625"},"PeriodicalIF":5.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TCDS.2024.3459314
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TCDS.2024.3459314","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3459314","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"C3-C3"},"PeriodicalIF":5.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716576","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TCDS.2024.3459316
{"title":"IEEE Transactions on Cognitive and Developmental Systems Information for Authors","authors":"","doi":"10.1109/TCDS.2024.3459316","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3459316","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"C4-C4"},"PeriodicalIF":5.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-14DOI: 10.1109/TCDS.2024.3459312
{"title":"IEEE Transactions on Cognitive and Developmental Systems Publication Information","authors":"","doi":"10.1109/TCDS.2024.3459312","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3459312","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 5","pages":"C2-C2"},"PeriodicalIF":5.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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