An Implementation of Vision Based Deep Reinforcement Learning for Humanoid Robot Locomotion

Recep OzalpÇaǧri KaymakÖzal YildirimAyşegül UçarYakup DemirCüneyt GüzelişOzaln, RecenYildirum, OzalGuzelis, CuneytKaymak, CagriUcar, AyscgulDemir, YakupP. Koprinkova-Hristova , T. Yildirim , V. Piuri , L. Iliadis , D. Camacho2025-10-062019978172811862810.1109/INISTA.2019.87782092-s2.0-85070740676https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070740676&doi=10.1109%2FINISTA.2019.8778209&partnerID=40&md5=db28d19538187e86361bfe93872b4962https://gcris.yasar.edu.tr/handle/123456789/9395https://doi.org/10.1109/INISTA.2019.8778209Deep reinforcement learning (DRL) exhibits a promising approach for controlling humanoid robot locomotion. However only values relating sensors such as IMU gyroscope and GPS are not sufficient robots to learn their locomotion skills. In this article we aim to show the success of vision based DRL. We propose a new vision based deep reinforcement learning algorithm for the locomotion of the Robotis-op2 humanoid robot for the first time. In experimental setup we construct the locomotion of humanoid robot in a specific environment in the Webots software. We use Double Dueling Q Networks (D3QN) and Deep Q Networks (DQN) that are a kind of reinforcement learning algorithm. We present the performance of vision based DRL algorithm on a locomotion experiment. The experimental results show that D3QN is better than DQN in that stable locomotion and fast training and the vision based DRL algorithms will be successfully able to use at the other complex environments and applications. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessControl, Deep Reinforcement Learning, Humanoid Robots, Locomotion Skills, Anthropomorphic Robots, Body Sensor Networks, Control Engineering, Intelligent Systems, Learning Algorithms, Machine Learning, Reinforcement Learning, Complex Environments, Humanoid Robot, Humanoid Robot Locomotion, Stable Locomotion, Vision Based, Webots Software, Deep LearningAnthropomorphic robots, Body sensor networks, Control engineering, Intelligent systems, Learning algorithms, Machine learning, Reinforcement learning, Complex environments, Humanoid robot, Humanoid robot locomotion, Stable locomotion, Vision based, Webots software, Deep learningLocomotion SkillscontrolDeep Reinforcement LearningHumanoid RobotsAn Implementation of Vision Based Deep Reinforcement Learning for Humanoid Robot LocomotionConference Object