Simge Nur AslanRecep OzalpAyşegül UçarCüneyt GüzelişUear, AysegulGuzelis, CunevtOzalp, RecepAslan, Simge NurM. Ivanovic , T. Yildirim , G. Trajcevski , C. Badica , L. Bellatreche , I. Kotenko , A. Badica , B. Erkmen , M. Savic2025-10-062020978172816799210.1109/INISTA49547.2020.91946302-s2.0-85091972373https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091972373&doi=10.1109%2FINISTA49547.2020.9194630&partnerID=40&md5=4a6875ab88a03e321e45fca288ee4de8https://gcris.yasar.edu.tr/handle/123456789/9170https://doi.org/10.1109/INISTA49547.2020.9194630Humanoid robots are deployed ranging from houses and hotels to healthcare and industry environments to help people. Robots can be easily programed by users to predefined tasks such as walking grasping stand-up and shake-up. However in these days all robots are expected to learn itself from the obtained experience by watching the environment and people in there. In this study it is aimed for Robotis-Op3 humanoid robot to grasp the objects by learning from demonstrations based on vision. A new algorithm is proposed for this purpose. Firstly the robot is manipulated from user commands and the raw images from the camera of Robotis-Op3 are collected. Secondly a semantic segmentation algorithm is applied to detect and recognize the objects. A new model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is then proposed to learn the user demonstrations. The results were compared in terms of training time performance and model complexity. Simulation results showed that new models produced a high performance for object manipulation. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessConvolutional Neural Networks, Humanoid Robots, Long Short-term Memory Networks, Object Grasping, Semantic Segmentation, Convolutional Neural Networks, Image Segmentation, Intelligent Systems, Object Detection, Semantics, Humanoid Robot, Industry Environment, Learning From Demonstration, Model Complexity, Object Manipulation, Semantic Segmentation, Short Term Memory, User Commands, Anthropomorphic RobotsConvolutional neural networks, Image segmentation, Intelligent systems, Object detection, Semantics, Humanoid robot, Industry environment, Learning from demonstration, Model complexity, Object manipulation, Semantic segmentation, Short term memory, User commands, Anthropomorphic robotsConvolutional Neural NetworksHumanoid RobotsSemantic SegmentationLong Short-Term Memory NetworksObject GraspingConference Object