IEEE Transactions on Robotics最新文献

{"title":"Stability and Transparency in Mixed-Reality Bilateral Human Teleoperation","authors":"David G. Black;Septimiu E. Salcudean","doi":"10.1109/TRO.2025.3613464","DOIUrl":"10.1109/TRO.2025.3613464","url":null,"abstract":"Recent work introduced the concept of human teleoperation (HT), where the remote robot typically considered in conventional bilateral teleoperation is replaced by a novice person wearing a mixed-reality head-mounted display and tracking the motion of a virtual tool controlled by an expert. HT has advantages in cost, complexity, and patient acceptance for telemedicine in low-resource communities or remote locations. However, the stability, transparency, and performance of bilateral HT are unexplored. In this article, we, therefore, develop a mathematical model of the HT system using test data. We then analyze various control architectures with this model and implement them with the HT system, testing volunteer operators and a virtual fixture-based simulated patient to find the achievable performance, investigate stability, and determine the most promising teleoperation scheme in the presence of time delays. We show that instability in HT, while not destructive or dangerous, makes the system impossible to use. However, stable and transparent teleoperation is possible with small time delays (<inline-formula><tex-math>$&lt; text{200}$</tex-math></inline-formula> ms) through three-channel teleoperation, or with large time delays through model-mediated teleoperation with local pose and force feedback for the novice.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5800-5815"},"PeriodicalIF":10.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127822","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":"Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysis","authors":"Yuanfei Lin, Zekun Xing, Xuyuan Han, Matthias Althoff","doi":"10.1109/tro.2025.3613550","DOIUrl":"https://doi.org/10.1109/tro.2025.3613550","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"13 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127825","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":"Grasp Like Humans: Learning Generalizable Multifingered Grasping From Human Proprioceptive Sensorimotor Integration","authors":"Ce Guo;Xieyuanli Chen;Zhiwen Zeng;Zirui Guo;Yihong Li;Haoran Xiao;Dewen Hu;Huimin Lu","doi":"10.1109/TRO.2025.3613541","DOIUrl":"10.1109/TRO.2025.3613541","url":null,"abstract":"Tactile and kinesthetic perceptions are crucial for human dexterous manipulation, enabling reliable grasping of objects via proprioceptive sensorimotor integration. For robotic hands, even though acquiring such tactile and kinesthetic feedback is feasible, establishing a direct mapping from this sensory feedback to motor actions remains challenging. In this article, we propose a novel glove-mediated tactile–kinematic perception–prediction framework for grasp skill transfer from human intuitive and natural operation to robotic execution based on imitation learning, and its effectiveness is validated through generalized grasping tasks, including those involving deformable objects. First, we integrate a data glove to capture tactile and kinesthetic data at the joint level. The glove is adaptable for both human and robotic hands, allowing data collection from natural human hand demonstrations across different scenarios. It ensures consistency in the raw data format, enabling evaluation of grasping for both human and robotic hands. Second, we establish a unified representation of multimodal inputs based on graph structures with polar coordinates. We explicitly integrate the morphological differences into the designed representation, enhancing the compatibility across different demonstrators and robotic hands. Furthermore, we introduce the tactile–kinesthetic spatio-temporal graph networks, which leverage multidimensional subgraph convolutions and attention-based long short-term memory (LSTM) layers to extract spatio-temporal features from graph inputs to predict node-based states for each hand joint. These predictions are then mapped to final commands through a force-position hybrid mapping. Comparative experiments and ablation studies demonstrate that our approach surpasses other methods in grasp success rate, finger coordination, contact force management, and both grasp and computational efficiency, achieving results most akin to human grasping. The robustness of our approach is also validated through multiple randomized experimental setups, and its generalization capability is tested across diverse objects and robotic hands.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5700-5719"},"PeriodicalIF":10.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127820","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":"Time-Varying Foot Placement Control for Humanoid Walking on Swaying Rigid Surface","authors":"Yuan Gao;Victor Paredes;Yukai Gong;Zijian He;Ayonga Hereid;Yan Gu","doi":"10.1109/TRO.2025.3612326","DOIUrl":"10.1109/TRO.2025.3612326","url":null,"abstract":"Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential to address for deploying humanoid robots in dynamic real-world environments such as moving trains, ships, and airplanes. This article introduces a real-time, provably stabilizing control approach for humanoid walking on periodically swaying rigid surface. The first key contribution is an analytical extension of the classical angular momentum-based linear inverted pendulum model from static to swaying grounds whose motion period may be different than the robot’s gait period. This extension results in a time-varying, nonhomogeneous robot model, which is fundamentally different from the existing pendulum models. We synthesize a discrete footstep control law for the model and derive a new set of sufficient stability conditions that verify the controller’s stabilizing effect. Finally, experiments conducted on a Digit humanoid robot, both in simulations and on hardware, demonstrate the framework’s effectiveness in addressing bipedal locomotion on swaying ground, even under uncertain surface motions and unknown external pushes.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5740-5760"},"PeriodicalIF":10.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173974","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089085","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}

{"title":"A Unilateral Active Knee Exoskeleton to Assist Individuals With Hemiparesis—A Pilot Study","authors":"Andrea Pergolini;Clara Beatriz Sanz-Morère;Chiara Livolsi;Matteo Fantozzi;Filippo Dell’Agnello;Tommaso Ciapetti;Alessandro Maselli;Andrea Baldoni;Emilio Trigili;Simona Crea;Nicola Vitiello","doi":"10.1109/TRO.2025.3610187","DOIUrl":"10.1109/TRO.2025.3610187","url":null,"abstract":"Most individuals who experience a stroke exhibit several sensorimotor impairments that limit their independence in everyday activities. Hemiparetic gait is frequently characterized by reduced knee flexion in swing due to knee stiffness or muscle weakness and knee hyperextension or knee buckling in the stance phase. Recently, unilateral-powered orthoses have been designed to overcome the limitations of the passive knee–ankle–foot orthoses. This study presents a unilateral active knee orthosis exoskeleton, AKO-β, endowed with a series-elastic actuator and designed to assist the knee in flexion and extension movements. In this article, we describe the system mechatronic design and its characterization on the bench, the control system, and pilot experiments with three poststroke participants. The device has a weight of 1.78 kg on the user’s leg, with a lateral encumbrance of 76 mm. The pilot experiments aimed to verify the effects of the exoskeleton assistance in hemiparetic gait patterns. When walking with the device, participants on average increased the knee flexion on the paretic side by 18.70° (+44.9%) during swing and decreased knee hyperextension in stance by 4.50°, compared to walking without it. Overall, when walking with the exoskeleton, subjects showed an improved gait variable score of the paretic knee profile by 37.5% compared to walking without it. The temporal and spatial gait symmetry indices did not show clear changes, although an improvement in symmetry was observed in two of the three participants. These preliminary results suggest the potential benefits of the unilateral active knee orthosis exoskeleton to enhance and restore mobility in individuals with hemiparetic gait.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5550-5563"},"PeriodicalIF":10.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072819","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":"High-Efficiency Vector Field by Time-Optimal Spatial Iterative Learning","authors":"Shuli Lv;Yan Gao;Quan Quan","doi":"10.1109/TRO.2025.3610174","DOIUrl":"10.1109/TRO.2025.3610174","url":null,"abstract":"This article presents a novel model-free spatial iterative learning (IL) framework to enhance the efficiency of vector field (VF) navigation for mobile robots. By integrating the idea of iterative learning control (ILC) control with VF, this framework utilizes historical data to enhance navigation efficiency significantly, reducing traversal time and expanding the applicability of IL to rapid navigation. Importantly, it has low-time complexity with <inline-formula><tex-math>$O(n)$</tex-math></inline-formula> per iteration, where <inline-formula><tex-math>$n$</tex-math></inline-formula> denotes the waypoints number, preventing the significant computational overhead caused by the increasing waypoints in existing methods, which often exceeds <inline-formula><tex-math>$O(n^{2})$</tex-math></inline-formula>, making it well-suited for real-time planning. Moreover, the approach is inherently model-free, leaning on historical data, thus enabling agile navigation with limited reliance on intricate model details. This article presents a comprehensive theoretical analysis of the stability, time optimality, time complexity, parameter insensitivity, robustness, and usage. Extensive simulations and experiments highlight its efficiency, promising a transformative impact on mobile robot navigation through the proposed IL.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5624-5644"},"PeriodicalIF":10.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072496","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":"Toward Accurate, Efficient, and Robust RGB-D Simultaneous Localization and Mapping in Challenging Environments","authors":"Hui Zhao;Fuqiang Gu;Jianga Shang;Xianlei Long;Jiarui Dou;Chao Chen;Huayan Pu;Jun Luo","doi":"10.1109/TRO.2025.3610173","DOIUrl":"10.1109/TRO.2025.3610173","url":null,"abstract":"Visual simultaneous localization and mapping (SLAM) is crucial to many applications such as self-driving vehicles and robot tasks. However, it is still challenging for existing visual SLAM approaches to achieve good performance in low-texture or illumination-changing scenes. In recent years, some researchers have turned to edge-based SLAM approaches to deal with the challenging scenes, which are more robust than feature-based and direct SLAM methods. Nevertheless, existing edge-based methods are computationally expensive and inferior than other visual SLAM systems in terms of accuracy. In this study, we propose EdgeSLAM, a novel RGB-D edge-based SLAM approach to deal with challenging scenarios that is efficient, accurate, and robust. EdgeSLAM is built on two innovative modules: efficient edge selection and adaptive robust motion estimation. The edge selection module can efficiently select a small set of edge pixels, which significantly improves the computational efficiency without sacrificing the accuracy. The motion estimation module improves the system’s accuracy and robustness by adaptively handling outliers in motion estimation. Extensive experiments were conducted on technical university of munich (TUM) RGBD, imperial college london (ICL)-National University of Ireland Maynooth (NUIM), and ETH zurich 3D reconstruction (ETH3D) datasets, and experimental results show that EdgeSLAM significantly outperforms five state-of-the-art methods in terms of efficiency, accuracy, and robustness, which achieves 29.17% accuracy improvements with a high processing speed of up to 120 frames/s and a high positioning success rate of 97.06%.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5720-5739"},"PeriodicalIF":10.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072817","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":"Predictive Body Awareness in Soft Robots: A Bayesian Variational Autoencoder Fusing Multimodal Sensory Data","authors":"Shuyu Wang;Dongling Liu;Changzeng Fu;Xiaoming Yuan;Peng Shan;Victor C.M. Leung","doi":"10.1109/TRO.2025.3610170","DOIUrl":"10.1109/TRO.2025.3610170","url":null,"abstract":"Predicting the causal flow by fusing multimodal perception is fundamental for constructing the bodily awareness of soft robots. However, forming such a predictive model while fusing the multimodal sensory data of soft robots remains challenging and less explored. In this study, we leverage the free energy principle within a Bayesian probabilistic deep learning framework to merge visual, pressure, and flex sensing signals. Our proposed multimodal association mechanism enhances the fusion process, establishing a robust computational methodology. We train the model using a newly collected dataset that captures the grasping dynamics of a soft gripper equipped with multimodal perception capabilities. By incorporating the current state and image differences, the forward model can predict the soft gripper’s physical interaction and movement in the image flow, which amounts to imagining future motion events. Moreover, we showcase effective predictions across modalities as well as for grasping outcomes. Notably, our enhanced variational autoencoder approach can pave the way for unprecedented possibilities of bodily awareness in soft robotics.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5663-5678"},"PeriodicalIF":10.5,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072495","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":"Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning","authors":"Andrey Zhitnikov, Vadim Indelman","doi":"10.1109/tro.2025.3610176","DOIUrl":"https://doi.org/10.1109/tro.2025.3610176","url":null,"abstract":"","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"36 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072818","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":"Plan Optimal Collision-Free Trajectories With Nonconvex Cost Functions Using Graphs of Convex Sets","authors":"Charles L. Clark;Biyun Xie","doi":"10.1109/TRO.2025.3610175","DOIUrl":"10.1109/TRO.2025.3610175","url":null,"abstract":"The recently developed approach to motion planning in graphs of convex sets (GCS) provides an efficient framework for computing shortest-distance collision-free paths using convex optimization. This new motion planner is notably more computationally efficient than popular sampling-based motion planners, but it does not support nonconvex cost functions. This article develops a novel motion planning algorithm, graph of convex sets with general costs (GCSGC), to solve this problem. A given nonconvex cost function is accurately approximated by a multiple-layer ReLU neural network and the configuration space is decomposed into a set of linear-cost regions using the hidden layers of the neural network. These linear-cost regions are intersected with a set of collision-free regions, and the resulting collision-free linear-cost regions are intersected to form the vertices and edges of the motion planner’s underlying graph structure. The edge costs have a closed-form solution within each collision-free linear-cost region, but it is nonconvex, so the McCormick relaxation is applied to convexify the edge costs. Finally, a graph preprocessing technique is developed to compute a representative graph structure that acts as a heuristic for the edge costs of the underlying GCS and then simplify the underlying graph structure by removing cycles and high-cost paths, which can significantly improve the efficiency of the planner and quality of the produced trajectories. The proposed motion planner is first validated in a 2-D configuration space with comparisons between different sized neural networks with and without preprocessing, comparisons between optimal trajectories from GCSGC with shortest-distance trajectories, and comparisons between GCSGC and GCS-Sequential linear programming (SLP). The GCSGC planner is further validated in a complex 7-D configuration space by comparing to state-of-the-art multiquery (PRM*, GCS-SLP) and single-query (TrajOpt, BIT*, AIT*, RRT*) planners. The results show that the proposed motion planner is very competitive in terms of computational efficiency, trajectory cost, and memory footprint. Two physical experiments further validate the effectiveness of the proposed motion planner in real-world motion planning applications.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":"41 ","pages":"5604-5623"},"PeriodicalIF":10.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072821","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}