Suchergebnisse - Learning from Demonstration Sensorimotor Learning Machine Learning for Robot Control~

Sensorimotor Learning With Stability Guarantees via Autonomous Neural Dynamic Policies

Autoren: Totsila, Dionis Chatzilygeroudis, Konstantinos Modugno, Valerio et al.

Weitere Verfasser: Totsila, Dionis Chatzilygeroudis, Konstantinos Modugno, Valerio et al.

Quelle: IEEE Robotics and Automation Letters. 10:1760-1767

Schlagwörter: Learning from Demonstration, Learning from Demonstration Sensorimotor Learning Machine Learning for Robot Control, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Sensorimotor Learning, Machine Learning for Robot Control

Dateibeschreibung: application/pdf

Zugangs-URL: https://inria.hal.science/hal-04913600v1
https://doi.org/10.1109/lra.2024.3524878
https://inria.hal.science/hal-04913600v1/document

Structured machine learning models for robustness against different factors of variability in robot control

Autoren: Davchev, Todor Bozhinov Hospedales, Timothy Ramamoorthy, Ram et al.

Weitere Verfasser: Davchev, Todor Bozhinov Hospedales, Timothy Ramamoorthy, Ram et al.

Schlagwörter: Robotics, Machine learning, Reinforcement Learning, Robot Learning, Learning from Demonstrations, Structured Machine Learning, Goal-conditioned Learning

Zugangs-URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.872272

Leveraging Motor Babbling for Efficient Robot Learning.

Autoren: Kase, Kei Matsumoto, Noboru Ogata, Tetsuya

Quelle: Journal of Robotics & Mechatronics; Oct2021, Vol. 33 Issue 5, p1063-1074, 12p

Schlagwörter: DEEP learning, ROBOT programming, ROBOTS, PROPRIOCEPTION, TASK performance

Efficient sensorimotor learning from multiple demonstrations.

Autoren: Nemec, Bojan Vuga, Rok Ude, Aleš

Quelle: Advanced Robotics. Nov2013, Vol. 27 Issue 13, p1023-1031. 9p.

Schlagwörter: *SENSORIMOTOR integration, *MACHINE learning, *PROBLEM solving, *ROBOTICS research, *CONFIGURATIONS (Geometry)

A combined learning and optimization framework to transfer human whole-body loco-manipulation skills to mobile manipulators.

Autoren: Zhao, Jianzhuang Tassi, Francesco Huang, Yanlong et al.

Quelle: Robotics & Autonomous Systems. Jul2025, Vol. 189, pN.PAG-N.PAG. 1p.

Schlagwörter: *MACHINE learning, *IMPEDANCE control, *QUADRATIC programming, *ORBITAL transfer (Space flight), *LEARNING, *WRIST

Learning for Adaptive and Reactive Robot Control : A Dynamical Systems Approach

Autoren: Aude Billard Sina Mirrazavi Nadia Figueroa

Resource Type: eBook.

Schlagworte: Autonomous robots, Machine learning, Robots--Control systems--Mathematical models

Categories: TECHNOLOGY & ENGINEERING / Robotics, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science

Neural Dynamic Policies for End-to-End Sensorimotor Learning

Autoren: Bahl, Shikhar Mukadam, Mustafa Gupta, Abhinav et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/2012.02788

Correspondence learning between morphologically different robots via task demonstrations

Autoren: Aktas, Hakan Nagai, Yukie Asada, Minoru et al.

Index Begriffe: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2310.13458

INTUITIVE LEARNING IN MULTI-ROBOT SYSTEMS

Autoren: Lakshmi Narayan Vishnunandan Venkatesh

Schlagwörter: Control engineering, mechatronics and robotics not elsewhere classified, Intelligent robotics, Artificial intelligence not elsewhere classified, Machine learning not elsewhere classified, Robotics, Multi-Robot Systems, human robot interaction (HRI), Large Language Model in Robotics, Machine Vision, Learning from Demonstration (LfD), Reinforcement Learning, Vision Language Models, Mobile Robots, Robot Learning

Verfügbarkeit: https://doi.org/10.25394/pgs.29367377.v1
https://figshare.com/articles/thesis/INTUITIVE_LEARNING_IN_MULTI-ROBOT_SYSTEMS/29367377

Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning

Autoren: Tagliabue, Andrea How, Jonathan P.

Index Begriffe: Computer Science - Robotics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2210.10127

Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Autoren: Hao, Yilun Wang, Ruinan Cao, Zhangjie et al.

Index Begriffe: Computer Science - Machine Learning, Computer Science - Robotics, text

URL: http://arxiv.org/abs/2209.07682

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation.

Autoren: Si, Weiyong Wang, Ning Li, Qinchuan et al.

Quelle: Frontiers in Neurorobotics; 2/11/2022, Vol. 16, p1-16, 16p

Schlagwörter: REMOTE control, IMPEDANCE control, SPACE robotics, JOB skills, HUMAN-robot interaction

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots.

Autoren: Teramae, Tatsuya Ishihara, Koji Babič, Jan et al.

Quelle: Frontiers in Neurorobotics; 1/24/2019, pN.PAG-N.PAG, 10p

Schlagwörter: MOTOR learning, ELECTROMYOGRAPHY, MUSCLE physiology, HUMAN-robot interaction, TASK performance

Brain-Inspired Hyperdimensional Computing in the Wild: Lightweight Symbolic Learning for Sensorimotor Controls of Wheeled Robots

Autoren: Kwon, Hyukjun Kim, Kangwon Lee, Junyoung et al.

Relation: Proceedings - IEEE International Conference on Robotics and Automation; http://hdl.handle.net/20.500.11750/57837

Verfügbarkeit: https://hdl.handle.net/20.500.11750/57837
https://doi.org/10.1109/ICRA57147.2024.10610176
https://icra2024.xsrv.jp/program/#Program-Overview

Brain-inspired biomimetic robot control: a review.

Autoren: Alepuz, Adrià Mompó Papageorgiou, Dimitrios Tolu, Silvia

Quelle: Frontiers in Neurorobotics; 2024, p1-13, 13p

Schlagwörter: ROBOT control systems, ARTIFICIAL neural networks, BRAINWASHING, SYSTEMS design, RESEARCH personnel, ROBOT hands, HUMANOID robots

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots.

Autoren: Teramae, Tatsuya Ishihara, Koji Babič, Jan et al.

Quelle: Frontiers in Neurorobotics; 11/6/2018, pN.PAG-N.PAG, 10p

Neurorobotics Workshop for High School Students Promotes Competence and Confidence in Computational Neuroscience.

Autoren: Harris, Christopher A. Guerri, Lucia Mircic, Stanislav et al.

Quelle: Frontiers in Neurorobotics; 2/13/2020, p1-10, 10p

Schlagwörter: COMPUTATIONAL neuroscience, HIGH school students, NEUROSCIENCES, EDUCATIONAL technology, EDUCATIONAL benefits, MACHINE learning, ROBOT control systems

Beyond Shared Autonomy: Joint Perception and Action for Human-In-The-Loop Mobile Robot Navigation Systems.

Autoren: Bozorgi, Hamed Ngo, Trung Dung

Quelle: Journal of Intelligent & Robotic Systems; Sep2023, Vol. 109 Issue 1, p1-25, 25p

Coupled Conditional Neural Movement Primitives.

Autoren: Pekmezci, Mehmet Ugur, Emre Oztop, Erhan

Quelle: Neural Computing & Applications; Oct2024, Vol. 36 Issue 30, p18999-19021, 23p

Schlagwörter: ROBOT motion, MOTOR ability, ROBOTS, DEEP learning, COUPLES

ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery.

Autoren: Balakuntala, Mythra V Gonzalez, Glebys T Wachs, Juan P et al.

Quelle: Military Medicine; 2023 Supplement, Vol. 188, p412-419, 8p

Schlagwörter: SURGICAL intensive care, MACHINE learning, REINFORCEMENT learning, SUPPORT vector machines, CRICOTHYROTOMY, DATABASES