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检索条件"机构=The Robot Learning Lab"
849 条 记 录,以下是1-10 订阅
排序:
LLM-controller: Dynamic robot control adaptation using large language models
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robotICS AND AUTONOMOUS SYSTEMS 2025年 186卷
作者: Zahedifar, Rasoul Baghshah, Mahdieh Soleymani Taheri, Alireza Sharif Univ Technol Mech Engn Dept Social & Cognit Robot Lab Tehran Iran Sharif Univ Technol Comp Engn Dept Machine Learning Lab Tehran Iran
In this study, a dynamic adaptation of a robot controller is investigated using large language models (LLMs). We propose our controller called the LLM-Controller, where, in response to changes in the system dynamics o... 详细信息
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Dynamic Electromagnetic Navigation
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IEEE robotICS AND AUTOMATION LETTERS 2025年 第6期10卷 6095-6102页
作者: Zughaibi, Jasan Nelson, Bradley J. Muehlebach, Michael Swiss Fed Inst Technol Multiscale Robot Lab CH-8092 Zurich Switzerland Max Planck Inst Intelligent Syst Learning & Dynam Syst Grp D-72076 Tubingen Germany
Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized i... 详细信息
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Unified Guidance and Jerk-Level Dynamic Inversion for Accurate Position Control of Hybrid UAVs
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IEEE TRANSACTIONS ON robotICS 2025年 41卷 708-728页
作者: Rohr, David Andersson, Olov Lawrance, Nicholas Stastny, Thomas Siegwart, Roland Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland KTH Royal Inst Technol Div Robot Percept & Learning S-10044 Stockholm Sweden CSIRO Data61 Robot Percept & Auton Grp Pullenvale Qld 4069 Australia
By combining rotary- and fixed-wing flight, hybrid uncrewed aerial vehicles (H-UAVs) can uniquely address missions combining long-range aerial transport and precise ground-relative tasks, such as the placement or retr... 详细信息
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Isolated Kalman filtering: theory and decoupled estimator design
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AUTONOMOUS robotS 2025年 第1期49卷 1-25页
作者: Jung, Roland Luft, Lukas Weiss, Stephan Univ Klagenfurt Control Networked Syst Grp Univ Str 65 A-9020 Klagenfurt Carinthia Austria Univ Freiburg Robot Learning Lab Georges Kohler Allee 80 D-79110 Freiburg Germany
In this paper, we propose a state decoupling strategy for Kalman filtering problems, when the dynamics of individual estimates are decoupled and their outputs are sparsely coupled. The algorithm is termed Isolated Kal... 详细信息
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Sensorimotor learning With Stability Guarantees via Autonomous Neural Dynamic Policies
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IEEE robotICS AND AUTOMATION LETTERS 2025年 第2期10卷 1760-1767页
作者: Totsila, Dionis Chatzilygeroudis, Konstantinos Modugno, Valerio Hadjivelichkov, Denis Kanoulas, Dimitrios Univ Lorraine Inria CNRS Loria F-54000 Nancy France Univ Patras Dept Math Computat Intelligence Lab CILab Patras 26504 Greece Univ Patras Dept Elect & Comp Engn Lab Automat & Robot LAR Patras 26504 Greece UCL Dept Comp Sci Robot Percept & Learning Lab RPL Lab London WC1E 6BT England Archimedes Athena RC Maroussi 15125 Greece
State-of-the-art sensorimotor learning algorithms, either in the context of reinforcement learning or imitation learning, offer policies that can often produce unstable behaviors, damaging the robot and/or the environ... 详细信息
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Rapid learning with phase-change memory-based in-memory computing through learning-to-learn
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NATURE COMMUNICATIONS 2025年 第1期16卷 1-16页
作者: Ortner, Thomas Petschenig, Horst Vasilopoulos, Athanasios Renner, Roland Brglez, Spela Limbacher, Thomas Pinero, Enrique Linares-Barranco, Alejandro Pantazi, Angeliki Legenstein, Robert IBM Res Europe Zurich Ruschlikon Switzerland Graz Univ Technol Inst Machine Learning & Neural Computat Graz Austria Univ Seville SCORE Lab EPS Robot & Technol Comp ETSII Seville Spain
There is a growing demand for low-power, autonomously learning artificial intelligence (AI) systems that can be applied at the edge and rapidly adapt to the specific situation at deployment site. However, current AI m...
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Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement learning Agents
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IEEE robotICS AND AUTOMATION LETTERS 2025年 第1期10卷 788-795页
作者: Bejarano, Federico Pizarro Brunke, Lukas Schoellig, Angela P. Univ Toronto Learning Syst & Robot Lab Toronto ON M5S 1A1 Canada Univ Toronto Robot Inst Toronto ON M5G 0C6 Canada Vector Inst Artificial Intelligence Toronto ON M5G 0C6 Canada Tech Univ Munich Munich Inst Robot & Machine Intelligence MIRMI D-80992 Munich Germany
Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee safety. Safety filters impart hard safety guarantees to RL controllers while maintaining flexibility. However, safety filters ca... 详细信息
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Optical Tactile Sensing for Aerial Multicontact Interaction: Design, Integration, and Evaluation
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IEEE TRANSACTIONS ON robotICS 2025年 41卷 364-377页
作者: Aucone, Emanuele Sferrazza, Carmelo Gregor, Manuel D'Andrea, Raffaello Mintchev, Stefano Swiss Fed Inst Technol Environm Robot Lab Dept Environm Syst Sci CH-8092 Zurich Switzerland Swiss Fed Inst Forest Snow & Landscape Res WSL CH-8903 Birmensdorf Switzerland Univ Calif Berkeley Robot Learning Lab Berkeley CA 94704 USA Swiss Fed Inst Technol Inst Dynam Syst & Control Dept Mech & Proc Engn CH-8092 Zurich Switzerland
Distributed tactile sensing for multiforce detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot prov... 详细信息
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TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年 第6期133卷 3781-3802页
作者: Xing, Jiazheng Xu, Chao Qian, Yijie Liu, Yang Dai, Guang Sun, Baigui Liu, Yong Wang, Jingdong Zhejiang Univ Coll Control Sci & Engn Lab Adv Percept Robot & Intelligent Learning Hangzhou 310027 Zhejiang Peoples R China Alibaba Grp Hangzhou Peoples R China State Grid Shaanxi Elect Power Co SGIT AI Lab Xian Peoples R China Baidu Inc Beijing Peoples R China
Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the clothing identity uncontrollability and... 详细信息
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One-Shot Dual-Arm Imitation learning
arXiv
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arXiv 2025年
作者: Wang, Yilong Johns, Edward The Robot Learning Lab Imperial College London United Kingdom
We introduce One-Shot Dual-Arm Imitation learning (ODIL), which enables dual-arm robots to learn precise and coordinated everyday tasks from just a single demonstration of the task. ODIL uses a new three-stage visual ... 详细信息
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