IEEE Transactions on Robotics最新文献_第2页

The Impact of Stress and Workload on Human Performance in Robot Teleoperation Tasks 机器人远程操作任务中压力和工作量对人类表现的影响

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-22 DOI: 10.1109/tro.2024.3484630

Yi Ting Sam, Erin Hedlund-Botti, Manisha Natarajan, Jamison Heard, Matthew Gombolay

引用次数: 0

Trust and Dependence on Robotic Decision Support 对机器人决策支持的信任和依赖

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-22 DOI: 10.1109/tro.2024.3484628

Manisha Natarajan, Matthew Gombolay

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SICNav: Safe and Interactive Crowd Navigation using Model Predictive Control and Bilevel Optimization SICNav：利用模型预测控制和双层优化实现安全互动的人群导航

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-22 DOI: 10.1109/tro.2024.3484634

Sepehr Samavi, James R. Han, Florian Shkurti, Angela P. Schoellig

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Augmented Maximum Correntropy Criterion for Robust Geometric Perception 用于稳健几何感知的增量最大熵标准

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-21 DOI: 10.1109/tro.2024.3484608

Jiayuan Li, Qingwu Hu, Xinyi Liu, Yongjun Zhang

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A Black-Box Physics-Informed Estimator Based on Gaussian Process Regression for Robot Inverse Dynamics Identification 基于高斯过程回归的黑盒物理信息估计器，用于机器人反动力学识别

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-07 DOI: 10.1109/TRO.2024.3474851

Giulio Giacomuzzo;Ruggero Carli;Diego Romeres;Alberto Dalla Libera

{"title":"A Black-Box Physics-Informed Estimator Based on Gaussian Process Regression for Robot Inverse Dynamics Identification","authors":"Giulio Giacomuzzo;Ruggero Carli;Diego Romeres;Alberto Dalla Libera","doi":"10.1109/TRO.2024.3474851","DOIUrl":"10.1109/TRO.2024.3474851","url":null,"abstract":"Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system is available. In this article, we propose a black-box model based on Gaussian process (GP) regression for the identification of the inverse dynamics of robotic manipulators. The proposed model relies on a novel multidimensional kernel, called \u0000<italic>Lagrangian Inspired Polynomial\u0000 (LIP) kernel. The LIP kernel is based on two main ideas. First, instead of directly modeling the inverse dynamics components, we model as GPs the kinetic and potential energy of the system. The GP prior on the inverse dynamics components is derived from those on the energies by applying the properties of GPs under linear operators. Second, as regards the energy prior definition, we prove a polynomial structure of the kinetic and potential energy, and we derive a polynomial kernel that encodes this property. As a consequence, the proposed model allows also to estimate the kinetic and potential energy without requiring any label on these quantities. Results on simulation and on two real robotic manipulators, namely a 7 DOF Franka Emika Panda, and a 6 DOF MELFA RV4FL, show that the proposed model outperforms state-of-the-art black-box estimators based both on Gaussian processes and neural networks in terms of accuracy, generality, and data efficiency. The experiments on the MELFA robot also demonstrate that our approach achieves performance comparable to fine-tuned model-based estimators, despite requiring less prior information. The code of the proposed model is publicly available.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384280","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}

引用次数: 0

Active Iterative Optimization for Aerial Visual Reconstruction of Wide-Area Natural Environment 用于广域自然环境航空视觉重建的主动迭代优化技术

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-07 DOI: 10.1109/tro.2024.3475213

Hongpeng Wang, Zhongzhi Cao, Yue Fei, Peizhao Wang, Yaojing Li, Chuanyu Sun, Ming He, Jianda Han

引用次数: 0

Approximate Methods for Visibility-Based Pursuit–Evasion 基于可见度的追逐-入侵近似方法

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-04 DOI: 10.1109/TRO.2024.3474948

Emmanuel Antonio;Israel Becerra;Rafael Murrieta-Cid

{"title":"Approximate Methods for Visibility-Based Pursuit–Evasion","authors":"Emmanuel Antonio;Israel Becerra;Rafael Murrieta-Cid","doi":"10.1109/TRO.2024.3474948","DOIUrl":"10.1109/TRO.2024.3474948","url":null,"abstract":"To the best of our knowledge, an exact solution to the visibility-based pursuit–evasion problem with point agents and polygonal obstacles addressed in this work is not known. Given the above, in this work, we present approximate algorithms, but feasible and with other desirable properties, for such a pursuit–evasion game. Our new method combines asymptotically optimal motion planning based on sampling (more specifically, optimal probabilistic roadmaps) and the value iteration of dynamic programming. In addition, our formulation finds solutions for the evader when there are singular surfaces, which previous work could not find. In this work, we obtain two main theoretical results. We first prove that the proposed discrete formulation is correct (that the method obtains the solution for the discretization of the given configuration space). Subsequently, combining random graph results, Bellman's optimality principle, and limits, it is proved that, as the number of samples tends to infinity, our approximate discrete formulation becomes the continuous formulation corresponding to the Hamilton–Jacobi–Isaacs equation. This results in a feasible method that improves its approximation as the number of samples increases. Simulation experiments in 2-D and 3-D environments with obstacles that are simply and multiplicattively connected exemplify the results of the new method and show the advantages over previous work.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377305","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}

引用次数: 0

Topology-Driven Parallel Trajectory Optimization in Dynamic Environments 动态环境中拓扑驱动的并行轨迹优化

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-04 DOI: 10.1109/tro.2024.3475047

Oscar de Groot, Laura Ferranti, Dariu M. Gavrila, Javier Alonso-Mora

引用次数: 0

MAGICVFM -Meta-Learning Adaptation for Ground Interaction Control With Visual Foundation Models MAGICVFM - 利用可视地基模型进行地面交互控制的元学习适配

IF 7.8 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-04 DOI: 10.1109/tro.2024.3475212

Elena Sorina Lupu, Fengze Xie, James A. Preiss, Jedidiah Alindogan, Matthew Anderson, Soon-Jo Chung

引用次数: 0

On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics 论机器人学中矩阵加权状态估计问题的半无限放松

IF 9.4 1区计算机科学

IEEE Transactions on Robotics Pub Date : 2024-10-04 DOI: 10.1109/TRO.2024.3475220

Connor Holmes;Frederike Dümbgen;Timothy Barfoot

{"title":"On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics","authors":"Connor Holmes;Frederike Dümbgen;Timothy Barfoot","doi":"10.1109/TRO.2024.3475220","DOIUrl":"10.1109/TRO.2024.3475220","url":null,"abstract":"In recent years, there has been remarkable progress in the development of so-called \u0000<italic>certifiable perception\u0000 methods, which leverage semidefinite, convex relaxations to find \u0000<italic>global optima\u0000 of perception problems in robotics. However, many of these relaxations rely on simplifying assumptions that facilitate the problem formulation, such as an \u0000<italic>isotropic\u0000 measurement noise distribution. In this article, we explore the tightness of the semidefinite relaxations of \u0000<italic>matrix-weighted\u0000 (anisotropic) state-estimation problems and reveal the limitations lurking therein: matrix-weighted factors can cause convex relaxations to lose tightness. In particular, we show that the semidefinite relaxations of localization problems with matrix weights may be tight only for low noise levels. To better understand this issue, we introduce a theoretical connection between the posterior uncertainty of the state estimate and the certificate matrix obtained via convex relaxation. With this connection in mind, we empirically explore the factors that contribute to this loss of tightness and demonstrate that \u0000<italic>redundant constraints\u0000 can be used to regain it. As a second technical contribution of this article, we show that the state-of-the-art relaxation of scalar-weighted simultaneous localization and mapping cannot be used when matrix weights are considered. We provide an alternate formulation and show that its semidefinite program relaxation is not tight (even for very low noise levels) unless specific \u0000<italic>redundant constraints\u0000 are used. We demonstrate the tightness of our formulations on both simulated and real-world data.","PeriodicalId":50388,"journal":{"name":"IEEE Transactions on Robotics","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377303","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}

引用次数: 0