IEEE Transactions on Robotics最新文献

{"title":"Transferring Grasping Across Grippers: Learning–Optimization Hybrid Framework for Generalized Planar Grasp Generation","authors":"Xianli Wang;Qingsong Xu","doi":"10.1109/TRO.2024.3422054","DOIUrl":"10.1109/TRO.2024.3422054","url":null,"abstract":"As diverse robotic hands keep emerging for industrial and household use, designing general grasp synthesis algorithms applicable to multiple grippers remains challenging. To improve the generality and effectiveness of multigripper planar grasping algorithms, we propose a grasping framework featuring gripper-agnostic scene inference and gripper-changeable optimization. In our approach, we introduce an interaction probability map that bridges the scene inference and grasp optimization modules. It efficiently decouples the learning of grasping knowledge and modeling of gripper's kinematics. The inference module adopts a modified directional ensemble method with a generated fingertip dataset to refine scene information. In grasp optimization, we formulate gripper-kinematic constraints for different grippers according to joint types. Extensive evaluations on the Cornell Grasping Dataset (with a success rate of 95.51%) and on multifingered grippers (ten grippers in the real world) demonstrate that our hybrid approach generalizes learnable knowledge across various grippers. This work enables the direct transfer of learned grasping knowledge to new grippers in real-world applications.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495665","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":"Quadratic Programming-Based Reference Spreading Control for Dual-Arm Robotic Manipulation With Planned Simultaneous Impacts","authors":"Jari van Steen;Gijs van den Brandt;Nathan van de Wouw;Jens Kober;Alessandro Saccon","doi":"10.1109/TRO.2024.3420800","DOIUrl":"10.1109/TRO.2024.3420800","url":null,"abstract":"With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tracking control of robotic manipulators that are tasked to perform nominally simultaneous impacts. This framework is an extension of the reference spreading (RS) control framework, in which overlapping ante- and post-impact references that are consistent with impact dynamics are defined. In this work, such a reference is constructed starting from a teleoperation-based approach. By using the corresponding ante- and post-impact control modes in the scope of a quadratic programming control approach, peaking of the velocity error and control inputs due to impacts is avoided while maintaining high tracking performance. With the inclusion of a novel interim mode, we aim to also avoid input peaks and steps when uncertainty in the environment causes a series of unplanned single impacts to occur rather than the planned simultaneous impact. This work in particular presents for the first time an experimental evaluation of RS control on a robotic setup, showcasing its robustness against uncertainty in the environment compared to three baseline control approaches.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462963","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":"Disturbance-Adaptive Tapered Soft Manipulator With Precise Motion Controller for Enhanced Task Performance","authors":"Xianglong Li;Quan Xiong;Dongbao Sui;Qinghua Zhang;Hongwu Li;Ziqi Wang;Tianjiao Zheng;Hesheng Wang;Jie Zhao;Yanhe Zhu","doi":"10.1109/TRO.2024.3420802","DOIUrl":"10.1109/TRO.2024.3420802","url":null,"abstract":"The field of soft manipulators requires a more promising solution, including efficient structures and controllers. This article presents a novel cable–pneumatic hybrid-driven tapered soft manipulator (TSM) design and control scheme to enhance the performance in actual tasks. This article is the first to present the design with a Bowden tube as a driving tendon and propose a composite tendon with Bowden tubes and cable tendons (BTCTs). Leveraging the principles of hybrid-driven antagonism, the compact TSM integrates the composite tendon with BTCTs and pneumatically actuated tapered bellows. This new hybrid-driven form provides the TSM with excellent resistance to axial extension, tangential bending, and torsion, enhancing the stiffness of the TSM. The variable-stiffness range of the TSM was quantified in tests, including axial stiffness (0.57–10.77 N/mm), tangential bending stiffness (0.01–0.45 N/mm), and torsion stiffness (0.02–0.044 N \u0000<inline-formula><tex-math>$cdot$</tex-math></inline-formula>\u0000 m/\u0000<inline-formula><tex-math>$^circ$</tex-math></inline-formula>\u0000) tests. A deep learning-based neural network approach was utilized to model the inverse kinematics of the TSM. For more precise motion control, using position and orientation feedback from the sensor at the tip, we have designed a closed-loop iterative feedback controller incorporating three algorithms. Experiments on spatial point positioning, trajectory tracking with different constraints, orientation control, and disturbance experiments were conducted on the TSM. Experimental results [spatial point positioning error (mean error of stable region: 0.17 mm), circular trajectory tracking error (mean and standard deviation (SD) of 100 trials: 0.87 \u0000<inline-formula><tex-math>$pm$</tex-math></inline-formula>\u0000 0.57 mm), orientation control error (less than 1\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000), and the performance in disturbance experiment] demonstrated that our approach has high control accuracy and strong robustness against external disturbances. We conducted experiments involving teleoperation control, collision-free precise operations in cluttered and constrained environments, and disturbance-adaptive board cleaning testing, ensuring both stability and safety during contact with humans. These experiments intuitively demonstrate the potential of this TSM for executing complex tasks in real-world environments, promising to become a safe collaborative assistant for humans in the future.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141463078","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":"Learning Human-Like Functional Grasping for Multifinger Hands From Few Demonstrations","authors":"Wei Wei;Peng Wang;Sizhe Wang;Yongkang Luo;Wanyi Li;Daheng Li;Yayu Huang;Haonan Duan","doi":"10.1109/TRO.2024.3420722","DOIUrl":"10.1109/TRO.2024.3420722","url":null,"abstract":"This article investigates the challenge of enabling multifinger hands to perform human-like functional grasping for various intentions. However, accomplishing functional grasping in real robot hands present many challenges, including handling generalization ability for kinematically diverse robot hands, generating intention-conditioned grasps for a large variety of objects, and incomplete perception from a single-view camera. In this work, we first propose a six-step functional grasp synthesis algorithm based on fine-grained contact modeling. With the fine-grained contact-based optimization and learned dense shape correspondence, the algorithm is adaptable to various objects of the same category and a wide range of multifinger hands using few demonstrations. Second, over 10 k functional grasps are synthesized to train our neural network, named DexFG-Net, which generates intention-conditioned grasps based on reconstructed object. Extensive experiments in the simulation and physical grasps indicate that the grasp synthesis algorithm can produce human-like functional grasp with robust stability and functionality, and the DexFG-Net can generate plausible and human-like intention-conditioned grasping postures for anthropomorphic hands.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462983","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":"How Safe Is Particle Filtering-Based Localization for Mobile Robots? An Integrity Monitoring Approach","authors":"Osama Abdul Hafez;Mathieu Joerger;Matthew Spenko","doi":"10.1109/TRO.2024.3420798","DOIUrl":"10.1109/TRO.2024.3420798","url":null,"abstract":"Deriving safe bounds on particle filter estimate is a research problem that, if solved, could greatly benefit robots in life-critical applications, a field that is facing increasing interest as more robots are being deployed near humans. In response, this article introduces a new fault detector and derives a performance measure for particle filter: integrity risk. Integrity risk is defined as the probability of having large estimate errors without triggering an alarm, all while considering measurement faults, unknown deterministic errors that cannot be modeled via normal white noise. In this work, the faults come in the form of incorrectly associated features when using the local nearest neighbors. Simulations and experiments assess the efficiency of the introduced safety metric. The results show that safety improves as map density increases as long as the number of particles is sufficient to shape the error distribution and the landmarks are well separated. Also, the results indicate that, when landmarks are poorly separated, particle filter is safer than Kalman filter, whereas, when landmarks are well separated, particle filter is often, but not always, safer than Kalman filter.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462975","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":"A Unified Motion Modeling Approach for Snake Robot's Gaits Generated With Backbone Curve Method","authors":"Wei Huang;Yongchun Fang;Xian Guo;Huawang Liu;Lixing Liu","doi":"10.1109/TRO.2024.3420803","DOIUrl":"10.1109/TRO.2024.3420803","url":null,"abstract":"In this article, a unified motion modeling approach for the 3-D snake robot is proposed, which enables motion prediction of all kinds of gaits generated by the backbone curve method on the ground. More specifically, the motion of the snake robot is novelly decomposed into two components, namely, the curve component and the shift component, which are explicitly related to the backbone curve's parameters and control's input. Considering the actual behavior of snake robots, a nonslip assumption is made to facilitate the modeling approach. Based on that, the ground-contacting points of the robot's links during shift control are conveniently analyzed, which helps to determine the moving direction of the curve components. Finally, with ground contacting points and backbone curve parameters determined, the characteristics of the two components, as well as the motion model, are successfully obtained. Utilizing this modeling approach, the widely used gaits, such as sidewinding, crawler, and S-pedal, are successfully modeled and then carefully analyzed to predict the movement of the snake robot with arbitrary given control input. Three groups of experiments are conducted, with the collected results showing the satisfactory accuracy of the obtained models. Compared with existing methods, the proposed modeling approach achieves a much more precise prediction, both in the direction and magnitude of snake robot motions.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462858","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":"HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots","authors":"Florenz Graf;Jochen Lindermayr;Birgit Graf;Werner Kraus;Marco F. Huber","doi":"10.1109/TRO.2024.3420799","DOIUrl":"10.1109/TRO.2024.3420799","url":null,"abstract":"Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This article presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such as a triplet perception split into recognition, knowledge representation, and knowledge interpretation. A recognition system splits the background and foreground to integrate exchangeable image-based object detectors and simultaneous localization and mapping, a multilayer knowledge base represents scene information in a hierarchical structure and offers interfaces for high-level control, and knowledge interpretation methods deploy spatio-temporal scene analysis and perceptual learning for self-adjustment. A single-setting ablation study is used to evaluate the impact of each component on the overall performance for a fetch-and-carry scenario in two simulated and one real-world environment.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462784","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":"Deformable Open-Frame Cable-Driven Parallel Robots: Modeling, Analysis, and Control","authors":"Arthur Ngo Foon Chan;Wuichung Cheng;Darwin Lau","doi":"10.1109/TRO.2024.3420714","DOIUrl":"10.1109/TRO.2024.3420714","url":null,"abstract":"This article proposes a generalized type of cable-driven parallel robot with deformable frames (D-CDPRs). The class of D-CDPRs allows: first, inevitable deformation of traditional rigid frame CDPRs to be considered; and second, new possibilities to develop CDPRs with lightweight frames that would deform. Comparatively, such lightweight CDPRs are easier to set up and largely reduce the cost of material and construction. However, the analysis and control of D-CDPRs are challenging as existing works usually assume the CDPR frame is rigid, such that the cable exit points on the frame are known and fixed. If the modeling errors induced by the deformable frame are not addressed appropriately, the control performance of D-CDPRs will be inaccurate and even unstable. To tackle this problem, novel modeling, analysis, and control approaches are proposed accordingly for D-CDPRs. Using the Euler–Bernoulli beam equations to develop a D-CDPR model, the workspace analysis is proposed and explored. Furthermore, the model-based feedforward length (MBFL) controller is proposed, where it is shown that cable length can be used to execute the tension control for D-CDPRs. Finally, the proposed work is validated in both simulation and hardware experiments.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462967","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":"Task and Motion Planning for Execution in the Real","authors":"Tianyang Pan;Rahul Shome;Lydia E. Kavraki","doi":"10.1109/TRO.2024.3418550","DOIUrl":"10.1109/TRO.2024.3418550","url":null,"abstract":"Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources such as occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare it against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data are shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141448733","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":"TossNet: Learning to Accurately Measure and Predict Robot Throwing of Arbitrary Objects in Real Time With Proprioceptive Sensing","authors":"Lipeng Chen;Weifeng Lu;Kun Zhang;Yizheng Zhang;Longfei Zhao;Yu Zheng","doi":"10.1109/TRO.2024.3416009","DOIUrl":"10.1109/TRO.2024.3416009","url":null,"abstract":"Accurate measuring and modeling of dynamic robot manipulation (e.g., tossing and catching) is particularly challenging, due to the inherent nonlinearity, complexity, and uncertainty in high-speed robot motions and highly dynamic robot–object interactions happening in very short distances and times. Most studies leverage extrinsic sensors such as visual and tactile feedback toward task or object-centric modeling of manipulation dynamics, which, however, may hit bottleneck due to the significant cost and complexity, e.g., the environmental restrictions. In this work, we investigate whether using solely the on-board proprioceptive sensory modalities can effectively capture and characterize dynamic manipulation processes. In particular, we present an object-agnostic strategy to learn the robot toss dynamics of arbitrary unknown objects from the spatio-temporal variations of robot toss movements and wrist-force/torque (F/T) observations. We then propose TossNet, an end-to-end formulation that jointly measures the robot toss dynamics and predicts the resulting flying trajectories of the tossed objects. Experimental results in both simulation and real-world scenarios demonstrate that our methods can accurately model the robot toss dynamics of both seen and unseen objects, and predict their flying trajectories with superior prediction accuracy in nearly real-time. Ablative results are also presented to demonstrate the effectiveness of each proprioceptive modality and their correlations in modeling the toss dynamics. Case studies show that TossNet can be applied on various real robot platforms for challenging tossing-centric robot applications, such as blind juggling and high-precise robot pitching.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462449","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}