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检索条件"主题词=Deep Learning in Robotics and Automation"
221 条 记 录,以下是1-10 订阅
排序:
Graph-Structured Super-Resolution for Geometry- Generalized Tomographic Tactile Sensing: Application to Humanoid Faces
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IEEE TRANSACTIONS ON robotics 2025年 41卷 558-572页
作者: Park, Hyunkyu Kim, Woojong Jeon, Sangha Na, Youngjin Kim, Jung Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Sookmyung Womens Univ Dept Mech Syst Engn Seoul 04310 South Korea
Electrical impedance tomographic (EIT) tactile sensing holds great promise for whole-body coverage of contact-rich robotic systems, offering extensive flexibility in sensor geometry. However, low spatial resolution re... 详细信息
来源: 评论
learning a Generalizable Trajectory Sampling Distribution for Model Predictive Control
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IEEE TRANSACTIONS ON robotics 2024年 40卷 2111-2127页
作者: Power, Thomas Berenson, Dmitry Univ Michigan Robot Dept Ann Arbor MI 48109 USA
We propose a sample-based model predictive control (MPC) method for collision-free navigation that uses a normalizing flow as a sampling distribution, conditioned on the start, goal, environment, and cost parameters. ... 详细信息
来源: 评论
The voraus-AD Dataset for Anomaly Detection in Robot Applications
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IEEE TRANSACTIONS ON robotics 2024年 40卷 438-451页
作者: Brockmann, Jan Thies Rudolph, Marco Rosenhahn, Bodo Wandt, Bastian Voraus Robot GmbH D-30167 Hannover Germany Leibniz Univ Hannover Inst Informat Proc L3S D-30167 Hannover Germany Linkoping Univ Comp Vis Lab Linkoping Sweden
During the operation of industrial robots, unusual events may endanger the safety of humans and the quality of production. When collecting data to detect such cases, it is not ensured that data from all potentially oc... 详细信息
来源: 评论
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation From Unlabeled Data
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IEEE TRANSACTIONS ON robotics 2024年 40卷 3146-3165页
作者: Williams, David S. W. Martini, Daniele De Gadd, Matthew Newman, Paul Univ Oxford Oxford Robot Inst Dept Engn Sci Oxford OX1 2JD England
Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a p... 详细信息
来源: 评论
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds
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IEEE TRANSACTIONS ON robotics 2024年 40卷 2357-2372页
作者: Zeller, Matthias Sandhu, Vardeep S. Mersch, Benedikt Behley, Jens Heidingsfeld, Michael Stachniss, Cyrill CARIAD SE D-53115 Bonn Germany Univ Bonn D-53115 Bonn Germany CARIAD SE D-71297 Monsheim Germany Univ Oxford Dept Engn Sci Oxford OX1 2JD England Lamarr Inst Machine Learning & Artificial Intellig Dortmund Germany
The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion i... 详细信息
来源: 评论
Goal-Conditioned Dual-Action Imitation learning for Dexterous Dual-Arm Robot Manipulation
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IEEE TRANSACTIONS ON robotics 2024年 40卷 2287-2305页
作者: Kim, Heecheol Ohmura, Yoshiyuki Kuniyoshi, Yasuo Univ Tokyo Grad Sch Informat Sci & Technol Lab Intelligent Syst & Informat Tokyo 1138654 Japan
Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledge about stable and dexterous manipula... 详细信息
来源: 评论
LeTac-MPC: learning Model Predictive Control for Tactile-Reactive Grasping
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IEEE TRANSACTIONS ON robotics 2024年 40卷 4376-4395页
作者: Xu, Zhengtong She, Yu Purdue Univ Sch Ind Engn W Lafayette IN 47906 USA
Grasping is a crucial task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects under various conditions and with differing physical properties. In this article,... 详细信息
来源: 评论
learning Cross Dimension Scene Representation for Interactive Navigation Agents in Obstacle-Cluttered Environments
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IEEE robotics AND automation LETTERS 2024年 第7期9卷 6264-6271页
作者: Sang, Hongrui Jiang, Rong Li, Xin Wang, Zhipeng Zhou, Yanmin He, Bin Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201203 Peoples R China
Embodied visual navigation has witnessed significant advancements. However, most studies commonly assume that environments are static and contain at least one collision-free path. In human environments, agents frequen... 详细信息
来源: 评论
Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot learning
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IEEE TRANSACTIONS ON robotics 2025年 41卷 926-945页
作者: Farid, Alec Veer, Sushant Pachisia, Divyanshu Majumdar, Anirudha Princeton Univ Princeton NJ 08540 USA Zoox Foster CA 94404 USA NVIDIA Autonomous Vehicle Res Grp Santa Clara CA 95051 USA Skydio San Mateo CA 94402 USA Princeton Univ Intelligent Robot Mot Lab Princeton NJ USA
Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably app... 详细信息
来源: 评论
COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving
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IEEE TRANSACTIONS ON robotics 2025年 41卷 1742-1754页
作者: Sanchez, Jules Deschaud, Jean-Emmanuel Goulette, Francois PSL Univ Ctr Robot Mines Paris PSL F-75006 Paris France ENSTA Paris Inst Polytech Paris U2IS F-91120 Palaiseau France
LiDAR semantic segmentation (LSS) for autonomous driving has been a growing field of interest in recent years. Datasets and methods have appeared and expanded very quickly, but methods have not been updated to exploit... 详细信息
来源: 评论