Search Results - Learning from Demonstration Sensorimotor Learning Machine Learning for Robot Control

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  1. 1

    Sensorimotor Learning With Stability Guarantees via Autonomous Neural Dynamic Policies by Totsila, Dionis, Chatzilygeroudis, Konstantinos, Modugno, Valerio, Hadjivelichkov, Denis, Kanoulas, Dimitrios

    ISSN: 2377-3766, 2377-3766
    Published: IEEE 01.02.2025
    Published in IEEE robotics and automation letters (01.02.2025)
    “…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…”
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    Journal Article
  2. 2

    Exploring Saliency for Learning Sensory-Motor Contingencies in Loco-Manipulation Tasks by Stefanini, Elisa, Lentini, Gianluca, Grioli, Giorgio, Catalano, Manuel Giuseppe, Bicchi, Antonio

    ISSN: 2218-6581, 2218-6581
    Published: Basel MDPI AG 01.04.2024
    Published in Robotics (Basel) (01.04.2024)
    “… This framework leverages tools from Learning from Demonstrations to have the robot memorize various sensory phases and corresponding motor actions…”
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    Journal Article
  3. 3

    Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations by Liang, Junchi, Wen, Bowen, Bekris, Kostas, Boularias, Abdeslam

    Published: IEEE 23.05.2022
    “…This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations…”
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    Conference Proceeding
  4. 4

    Socially guided intrinsic motivation for robot learning of motor skills by Nguyen, Sao Mai, Oudeyer, Pierre-Yves

    ISSN: 0929-5593, 1573-7527
    Published: Boston Springer US 01.03.2014
    Published in Autonomous robots (01.03.2014)
    “… Our algorithmic architecture, called SGIM-D , allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised…”
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    Journal Article
  5. 5
  6. 6

    Augmented Reality-Driven Machine Learning Techniques for Gripper-Integrated Robotics by Acar, Ramazan Onur, Talha Basibuyuk, M., Bahar, Serdar, Ugur, Emre, Yildirim, Yigit, Dogan, Mustafa Doga

    Published: IEEE 25.06.2025
    “…Learning from Demonstration (LfD) is a widely used method for teaching control policies to robots…”
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    Conference Proceeding
  7. 7

    Classifying a Sensorimotor Skill of Periodontal Probing by Babushkin, Vahan, Hassan Jamil, Muhammad, Sefo, Dianne L., Loomer, Peter M., Eid, Mohamad

    ISSN: 2767-7745
    Published: IEEE 10.02.2023
    “… To ensure efficient communication of a sensorimotor skill, a model that captures the skill's main features and provides real-time feedback and guidance based on the user's expertise is desirable…”
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    Conference Proceeding
  8. 8

    Neural Dynamic Policies for End-to-End Sensorimotor Learning by Bahl, Shikhar, Mukadam, Mustafa, Gupta, Abhinav, Pathak, Deepak

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 04.12.2020
    Published in arXiv.org (04.12.2020)
    “…The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces such as torque, joint angle, or end-effector position…”
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    Paper
  9. 9

    Humanoid robot posture-control learning in real-time based on human sensorimotor learning ability by Peternel, Luka, Babic, Jan

    ISBN: 1467356417, 9781467356411
    ISSN: 1050-4729
    Published: IEEE 01.05.2013
    “… The key element of the system is exploitation of the human sensorimotor learning ability where a human demonstrator learns how to operate a robot in the same fashion as humans adapt to various everyday tasks…”
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    Conference Proceeding
  10. 10

    Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills by Sao Mai Nguyen, Pierre-Yves Oudeyer

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 19.04.2018
    Published in arXiv.org (19.04.2018)
    “… Our architecture, called SGIM-D, allows efficient learning of high-dimensional continuous sensorimotor inverse models in robots, and in particular learns distributions of parameterised motor policies…”
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    Paper
  11. 11

    GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images by Singh, Avi, Yang, Larry, Levine, Sergey

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 07.08.2017
    Published in arXiv.org (07.08.2017)
    “…We tackle the problem of learning robotic sensorimotor control policies that can generalize to visually diverse and unseen environments…”
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    Paper
  12. 12

    Motor Skill Learning with Local Trajectory Methods by Levine, Sergey

    ISBN: 9798698535164
    Published: ProQuest Dissertations & Theses 01.01.2014
    “…Motor or sensorimotor skills are behaviors that require close coordination of motor control with feedback from the environment…”
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    Dissertation