Author

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

Ozal, R.; Kaymak, C.; Yildirim, O.; Ucar, A.; Demir, Y.; Guzelis, C. (IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings, 2019)

Deep 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 ...

Learning to move an object by the humanoid robots by using deep reinforcement learning

Aslan, S.N.; Tasci, B.; Ucar, A.; Guzelis, C. (IOS Press, 2021)

This paper proposes an algorithm for learning to move the desired object by humanoid robots. In this algorithm, the semantic segmentation algorithm and Deep Reinforcement Learning (DRL) algorithms are combined. The semantic ...

New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

Aslan, S.N.; Ozalp, R.; Ucar, A.; Guzelis, C. (Springer, 2021)

As the environments that human live are complex and uncontrolled, the object manipulation with humanoid robots is regarded as one of the most challenging tasks. Learning a manipulation skill from human Demonstration (LfD) ...

New convolutional neural network models for efficient object recognition with humanoid robots

Aslan, S.N.; Ucar, A.; Guzelis, C. (Taylor and Francis Inc., 2021)

Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is ...

Object recognition and detection with deep learning for autonomous driving applications

Ucar, A.; Demir, Y.; Guzelis, C. (Simulation, 2017)

Autonomous driving requires reliable and accurate detection and recognition of surrounding objects in real drivable environments. Although different object detection algorithms have been proposed, not all are robust enough ...

Object recognition and detection with deep learning for autonomous driving applications

Ucar, A.; Demir, Y.; Guzelis, C. (SAGE PUBLICATIONS LTD, 2017)

Autonomous driving requires reliable and accurate detection and recognition of surrounding objects in real drivable environments. Although different object detection algorithms have been proposed, not all are robust enough ...

Author

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

Learning to move an object by the humanoid robots by using deep reinforcement learning ﻿

New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration ﻿

New convolutional neural network models for efficient object recognition with humanoid robots ﻿

Object recognition and detection with deep learning for autonomous driving applications ﻿

Object recognition and detection with deep learning for autonomous driving applications ﻿

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

Learning to move an object by the humanoid robots by using deep reinforcement learning

New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration

New convolutional neural network models for efficient object recognition with humanoid robots

Object recognition and detection with deep learning for autonomous driving applications

Object recognition and detection with deep learning for autonomous driving applications