Pub Date : 2026-01-14DOI: 10.1109/tro.2026.3653859
Peiyi Wang, Daniel Feliu-Talegon, Yuchen Sun, Zhexin Xie, Wenci Xin, Muhammad Sunny Nazeer, Cosimo Della Santina, Cecilia Laschi, Federico Renda
{"title":"Strain-based Shape and 3D Force Estimation for Rod-driven Continuum Robots with Stretch Sensors","authors":"Peiyi Wang, Daniel Feliu-Talegon, Yuchen Sun, Zhexin Xie, Wenci Xin, Muhammad Sunny Nazeer, Cosimo Della Santina, Cecilia Laschi, Federico Renda","doi":"10.1109/tro.2026.3653859","DOIUrl":"https://doi.org/10.1109/tro.2026.3653859","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"177 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972401","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 : 2026-01-14DOI: 10.1109/TRO.2026.3653783
Xingyu Chen;Xiaotian Shi;Peinan Yan;Jieji Ren;Guoying Gu;Jiang Zou
Due to their continuous electromechanical deformation, rate-dependent viscoelasticity, and complex mechanical vibration, dynamic modeling and high-speed tracking control of dielectric elastomer actuators (DEAs) remain elusive, significantly limiting their working bandwidth. In this work, we propose a physics-informed token prediction (PITP) that enables accurate modeling of DEA dynamics and high-speed feedforward tracking control. The PITP framework consists of two key components: a physics-informed encoder and a dynamic decoder. The physics-informed encoder is designed based on a simplified equivalent linear model and trained through the hierarchical optimization training method, which embeds the global dynamic characteristics into tokens, minimizing the need for extensive data and training. Then, the dynamic decoder is developed by using these tokens as state-dependent parameters, capable of describing complex dynamic responses through the autoregressive solution. Finally, by taking advantage of the model’s reversibility, a direct inverse compensator is established to linearize the input–output relationship. Experimental results of several DEAs with different configurations and payloads demonstrate that, based on our PITP framework, the complex nonlinear dynamic responses of all DEAs can be precisely described and eliminated within their natural frequency, validating its generality and versatility. By leveraging fast modeling ($< $30 min) and high-speed feedforward tracking control, our PITP framework may accelerate DEAs’ practical applications.
{"title":"Physics-Informed Token Prediction-Based Dynamic Modeling and High-Speed Feedforward Tracking Control of Dielectric Elastomer Actuators","authors":"Xingyu Chen;Xiaotian Shi;Peinan Yan;Jieji Ren;Guoying Gu;Jiang Zou","doi":"10.1109/TRO.2026.3653783","DOIUrl":"10.1109/TRO.2026.3653783","url":null,"abstract":"Due to their continuous electromechanical deformation, rate-dependent viscoelasticity, and complex mechanical vibration, dynamic modeling and high-speed tracking control of dielectric elastomer actuators (DEAs) remain elusive, significantly limiting their working bandwidth. In this work, we propose a physics-informed token prediction (PITP) that enables accurate modeling of DEA dynamics and high-speed feedforward tracking control. The PITP framework consists of two key components: a physics-informed encoder and a dynamic decoder. The physics-informed encoder is designed based on a simplified equivalent linear model and trained through the hierarchical optimization training method, which embeds the global dynamic characteristics into tokens, minimizing the need for extensive data and training. Then, the dynamic decoder is developed by using these tokens as state-dependent parameters, capable of describing complex dynamic responses through the autoregressive solution. Finally, by taking advantage of the model’s reversibility, a direct inverse compensator is established to linearize the input–output relationship. Experimental results of several DEAs with different configurations and payloads demonstrate that, based on our PITP framework, the complex nonlinear dynamic responses of all DEAs can be precisely described and eliminated within their natural frequency, validating its generality and versatility. By leveraging fast modeling (<inline-formula><tex-math>$< $</tex-math></inline-formula>30 min) and high-speed feedforward tracking control, our PITP framework may accelerate DEAs’ practical applications.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"839-855"},"PeriodicalIF":10.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972402","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}
{"title":"Stable Kinematics for Multi-Robot Collaborative Transporting System with a Deformable Sheet","authors":"Wenyao Ma, Jiawei Hu, Jiamao Li, Jingang Yi, Zhenhua Xiong","doi":"10.1109/tro.2026.3653870","DOIUrl":"https://doi.org/10.1109/tro.2026.3653870","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"7 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145972367","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 : 2026-01-12DOI: 10.1109/TRO.2026.3651683
Ajay Suresha Sathya;Justin Carpentier
Efficient rigid-body dynamics algorithms are instrumental in enabling high-frequency dynamics evaluation for resource-intensive applications (e.g., model-predictive control, large-scale simulation, and reinforcement learning), potentially on resource-constrained hardware. Existing recursive algorithms with low computational complexity are mostly restricted to kinematic trees with external contact constraints or are sensitive to singular cases (e.g., linearly dependent constraints and kinematic singularities), severely impacting their practical usage in existing simulators. This article introduces two original low-complexity recursive algorithms: the loop-constrained articulated body algorithm and proximal BBO (Brandl, Bae, and others), both based on a proximal dynamics formulation for forward simulation of closed-loop mechanisms. These algorithms are derived from first principles using nonserial dynamic programming, exhibit linear complexity in practical scenarios, and are numerically robust in the face of singular cases. They extend the existing constrained articulated body algorithm to handle internal loops and the pioneering BBO algorithm from the 1980s to singular cases. Both algorithms have been implemented by leveraging the open-source Pinocchio library, benchmarked in detail, and demonstrate state-of-the-art performance for various robot topologies, including over $6times$ speed-ups compared to existing nonrecursive algorithms for high-degree-of-freedom systems with internal loops, such as recent humanoid robots.
{"title":"Constrained Articulated Body Algorithms for Closed-Loop Mechanisms","authors":"Ajay Suresha Sathya;Justin Carpentier","doi":"10.1109/TRO.2026.3651683","DOIUrl":"10.1109/TRO.2026.3651683","url":null,"abstract":"Efficient rigid-body dynamics algorithms are instrumental in enabling high-frequency dynamics evaluation for resource-intensive applications (e.g., model-predictive control, large-scale simulation, and reinforcement learning), potentially on resource-constrained hardware. Existing recursive algorithms with low computational complexity are mostly restricted to kinematic trees with external contact constraints or are sensitive to singular cases (e.g., linearly dependent constraints and kinematic singularities), severely impacting their practical usage in existing simulators. This article introduces two original low-complexity recursive algorithms: the loop-constrained articulated body algorithm and proximal BBO (Brandl, Bae, and others), both based on a proximal dynamics formulation for forward simulation of closed-loop mechanisms. These algorithms are derived from first principles using nonserial dynamic programming, exhibit linear complexity in practical scenarios, and are numerically robust in the face of singular cases. They extend the existing constrained articulated body algorithm to handle internal loops and the pioneering BBO algorithm from the 1980s to singular cases. Both algorithms have been implemented by leveraging the open-source Pinocchio library, benchmarked in detail, and demonstrate state-of-the-art performance for various robot topologies, including over <inline-formula><tex-math>$6times$</tex-math></inline-formula> speed-ups compared to existing nonrecursive algorithms for high-degree-of-freedom systems with internal loops, such as recent humanoid robots.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"819-838"},"PeriodicalIF":10.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955199","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 : 2026-01-12DOI: 10.1109/TRO.2026.3651669
Qingtao Liu;Zhengnan Sun;Yu Cui;Haoming Li;Gaofeng Li;Lin Shao;Jiming Chen;Qi Ye
Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multifingered robotic hands. Many existing deep reinforcement learning-based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this article, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e., grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multicamera and single-camera setups and demonstrate a small sim-to-real gap.
{"title":"DexRepNet++: Learning Dexterous Robotic Manipulation With Geometric and Spatial Hand-Object Representations","authors":"Qingtao Liu;Zhengnan Sun;Yu Cui;Haoming Li;Gaofeng Li;Lin Shao;Jiming Chen;Qi Ye","doi":"10.1109/TRO.2026.3651669","DOIUrl":"10.1109/TRO.2026.3651669","url":null,"abstract":"Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multifingered robotic hands. Many existing deep reinforcement learning-based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this article, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e., grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multicamera and single-camera setups and demonstrate a small sim-to-real gap.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"42 ","pages":"799-818"},"PeriodicalIF":10.5,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955196","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}
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