Výsledky vyhledávání - Learning from Demonstration Sensorimotor Learning Machine Learning for Robot Control

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

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

“… This framework leverages tools from Learning from Demonstrations to have the robot memorize various sensory phases and corresponding motor actions…”
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Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations Autor Liang, Junchi, Wen, Bowen, Bekris, Kostas, Boularias, Abdeslam

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

“… 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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Brain-Inspired Hyperdimensional Computing in the Wild: Lightweight Symbolic Learning for Sensorimotor Controls of Wheeled Robots Autor Kwon, Hyukjun, Kim, Kangwon, Lee, Junyoung, Lee, Hyunsei, Kim, Jiseung, Kim, Jinhyung, Kim, Taehyung, Kim, Yongnyeon, Ni, Yang, Imani, Mohsen, Suh, Ilhong, Kim, Yeseong

“… We introduce ReactHD, an HDC-based framework tailored for perception-action-based learning for sensorimotor controls of robot tasks…”
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Augmented Reality-Driven Machine Learning Techniques for Gripper-Integrated Robotics Autor Acar, Ramazan Onur, Talha Basibuyuk, M., Bahar, Serdar, Ugur, Emre, Yildirim, Yigit, Dogan, Mustafa Doga

“…Learning from Demonstration (LfD) is a widely used method for teaching control policies to robots…”
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Classifying a Sensorimotor Skill of Periodontal Probing Autor Babushkin, Vahan, Hassan Jamil, Muhammad, Sefo, Dianne L., Loomer, Peter M., Eid, Mohamad

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

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

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

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

“…We tackle the problem of learning robotic sensorimotor control policies that can generalize to visually diverse and unseen environments…”
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Motor Skill Learning with Local Trajectory Methods Autor Levine, Sergey

“…Motor or sensorimotor skills are behaviors that require close coordination of motor control with feedback from the environment…”
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