The capability for robotic systems to rearrange objects based on human instructions represents a critical step toward realizing embodied intelligence. Recently, diffusion-based learning has shown significant advancements in the field of data generation while prompt-based learning has proven effective in formulating robot manipulation strategies. However, prior solutions for robotic rearrangement have overlooked the significance of integrating human preferences and optimizing for rearrangement efficiency. Additionally, traditional prompt-based approaches struggle with complex, semantically meaningful rearrangement tasks without predefined target states for objects. To address these challenges, our work first introduces a comprehensive two dimensional (2-D) tabletop rearrangement dataset, utilizing a physical simulator to capture interobject relationships and semantic configurations. Then, we present DreamArrangement, a novel language-conditioned object rearrangement scheme, consisting of two primary processes: employing a transformer-based multimodal denoising diffusion model to envisage the desired arrangement of objects, and leveraging a vision–language foundational model to derive actionable policies from text, alongside initial and target visual information. In particular, we introduce an efficiency-oriented learning strategy to minimize the average motion distance of objects. Given few-shot instruction examples, the learned policy from our synthetic dataset can be transferred to the real world without extra human intervention. Extensive simulations validate DreamArrangement’s superior rearrangement quality and efficiency. Moreover, real-world robotic experiments confirm that our method can adeptly execute a range of challenging, language-conditioned, and long-horizon tasks with a singular model. The demonstration video can be found at https://youtu.be/fq25-DjrbQE
{"title":"DreamArrangement: Learning Language-Conditioned Robotic Rearrangement of Objects via Denoising Diffusion and VLM Planner","authors":"Wenkai Chen;Changming Xiao;Ge Gao;Fuchun Sun;Changshui Zhang;Jianwei Zhang","doi":"10.1109/TSMC.2025.3611698","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611698","url":null,"abstract":"The capability for robotic systems to rearrange objects based on human instructions represents a critical step toward realizing embodied intelligence. Recently, diffusion-based learning has shown significant advancements in the field of data generation while prompt-based learning has proven effective in formulating robot manipulation strategies. However, prior solutions for robotic rearrangement have overlooked the significance of integrating human preferences and optimizing for rearrangement efficiency. Additionally, traditional prompt-based approaches struggle with complex, semantically meaningful rearrangement tasks without predefined target states for objects. To address these challenges, our work first introduces a comprehensive two dimensional (2-D) tabletop rearrangement dataset, utilizing a physical simulator to capture interobject relationships and semantic configurations. Then, we present DreamArrangement, a novel language-conditioned object rearrangement scheme, consisting of two primary processes: employing a transformer-based multimodal denoising diffusion model to envisage the desired arrangement of objects, and leveraging a vision–language foundational model to derive actionable policies from text, alongside initial and target visual information. In particular, we introduce an efficiency-oriented learning strategy to minimize the average motion distance of objects. Given few-shot instruction examples, the learned policy from our synthetic dataset can be transferred to the real world without extra human intervention. Extensive simulations validate DreamArrangement’s superior rearrangement quality and efficiency. Moreover, real-world robotic experiments confirm that our method can adeptly execute a range of challenging, language-conditioned, and long-horizon tasks with a singular model. The demonstration video can be found at <uri>https://youtu.be/fq25-DjrbQE</uri>","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8675-8688"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-24DOI: 10.1109/TSMC.2025.3611946
Junxiang Chen;Yujie Guo;Xiangdong Kong;Kelong Xu;Chao Ai
This study investigates the trajectory optimization and high-precision motion control of excavators based on an independent metering hydraulic system. Considering both operational efficiency and motion smoothness, we propose a motion control method for excavator manipulators based on time-energy-jerk integrated optimal trajectory planning. The nondominated sorting genetic algorithm II (NSGA-II) algorithm is used to optimize interpolated trajectory based on five-time B-splines in the joint space. To ensure that excavators can accurately execute the planned optimal trajectory, the corresponding arms must be controlled with high precision. The oil inlet flow and the oil return pressure controllers are designed based on the independent metering hydraulic system. The flow controller is designed based on time-logarithmic barrier Lyapunov function to determine the virtual control rate and uses the Levant filter for filtering. The corresponding error transformations are employed to avoid the problem of the explosion of complexity in the traditional backstepping controller designs while ensuring that transient behavior of system tracking errors remains within specified boundaries. The uncertain components and nonlinear functions in the manipulator system are approximated by neural network (NN). Additionally, the pressure controller is used to keep the oil return pressure low to reduce system’s energy consumption. Finally, comparative simulations are conducted to verify the superiority of the proposed controller.
{"title":"Trajectory Planning and High-Precision Motion Control of Excavators Based on Independent Metering Hydraulic Configuration","authors":"Junxiang Chen;Yujie Guo;Xiangdong Kong;Kelong Xu;Chao Ai","doi":"10.1109/TSMC.2025.3611946","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611946","url":null,"abstract":"This study investigates the trajectory optimization and high-precision motion control of excavators based on an independent metering hydraulic system. Considering both operational efficiency and motion smoothness, we propose a motion control method for excavator manipulators based on time-energy-jerk integrated optimal trajectory planning. The nondominated sorting genetic algorithm II (NSGA-II) algorithm is used to optimize interpolated trajectory based on five-time B-splines in the joint space. To ensure that excavators can accurately execute the planned optimal trajectory, the corresponding arms must be controlled with high precision. The oil inlet flow and the oil return pressure controllers are designed based on the independent metering hydraulic system. The flow controller is designed based on time-logarithmic barrier Lyapunov function to determine the virtual control rate and uses the Levant filter for filtering. The corresponding error transformations are employed to avoid the problem of the explosion of complexity in the traditional backstepping controller designs while ensuring that transient behavior of system tracking errors remains within specified boundaries. The uncertain components and nonlinear functions in the manipulator system are approximated by neural network (NN). Additionally, the pressure controller is used to keep the oil return pressure low to reduce system’s energy consumption. Finally, comparative simulations are conducted to verify the superiority of the proposed controller.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8689-8700"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-23DOI: 10.1109/TSMC.2025.3611700
Pengfei Guo;Yunong Zhang;Min Yang;Zheng-An Yao;Shuai Li
Time-dependent Lyapunov matrix equation (TDLME) plays a central role in the control of linear and nonlinear systems. Existing models, including the classical zeroing neural dynamics (ZNDs) model and its variants, have been used to address the TDLME problem. However, those models require time-dependent matrix inversion, which is computationally demanding, and they primarily focus on measurement-related noise, overlooking other sources of system uncertainty. To overcome these challenges, we propose an inverse-free reciprocal-type ZND (RTZND) model. This model integrates an energy-based error function with the ZND framework, eliminating the need for matrix inversion and incorporating error-feedback-related noise through its closed-loop control structure. We establish the convergence and robustness of the RTZND model using Lyapunov stability theory and assess its performance under external disturbances. Numerical simulations confirm its effectiveness and improved computational efficiency in solving the TDLME problem. We further confirm its applicability through two case studies, a time-dependent linear system and a nonlinear system modeled by the single machine infinite bus (SMIB) system, highlighting the RTZND model’s practical value in addressing TDLME problems.
{"title":"Reciprocal-Type Zeroing Neural Dynamics Model for Tackling Time-Dependent Lyapunov Matrix Equation Problems and Applications","authors":"Pengfei Guo;Yunong Zhang;Min Yang;Zheng-An Yao;Shuai Li","doi":"10.1109/TSMC.2025.3611700","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611700","url":null,"abstract":"Time-dependent Lyapunov matrix equation (TDLME) plays a central role in the control of linear and nonlinear systems. Existing models, including the classical zeroing neural dynamics (ZNDs) model and its variants, have been used to address the TDLME problem. However, those models require time-dependent matrix inversion, which is computationally demanding, and they primarily focus on measurement-related noise, overlooking other sources of system uncertainty. To overcome these challenges, we propose an inverse-free reciprocal-type ZND (RTZND) model. This model integrates an energy-based error function with the ZND framework, eliminating the need for matrix inversion and incorporating error-feedback-related noise through its closed-loop control structure. We establish the convergence and robustness of the RTZND model using Lyapunov stability theory and assess its performance under external disturbances. Numerical simulations confirm its effectiveness and improved computational efficiency in solving the TDLME problem. We further confirm its applicability through two case studies, a time-dependent linear system and a nonlinear system modeled by the single machine infinite bus (SMIB) system, highlighting the RTZND model’s practical value in addressing TDLME problems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8715-8728"},"PeriodicalIF":8.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604199
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2025.3604199","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604199","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3606176
{"title":"Thank You for Your Authorship","authors":"","doi":"10.1109/TSMC.2025.3606176","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3606176","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7010-7010"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173428","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604203
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3604203","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604203","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173429","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604195
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/TSMC.2025.3604195","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604195","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7008-7008"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604187
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information","authors":"","doi":"10.1109/TSMC.2025.3604187","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604187","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"C2-C2"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604211
{"title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors","authors":"","doi":"10.1109/TSMC.2025.3604211","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604211","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"C4-C4"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-09-19DOI: 10.1109/TSMC.2025.3604201
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TSMC.2025.3604201","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3604201","url":null,"abstract":"","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"C3-C3"},"PeriodicalIF":8.7,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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