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Object recognition and detection with deep learning for autonomous driving applications
(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 ...
New convolutional neural network models for efficient object recognition with humanoid robots
(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 ...
Learning to move an object by the humanoid robots by using deep reinforcement learning
(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 ...
An Implementation of Vision Based Deep Reinforcement Learning for Humanoid Robot Locomotion
(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 ...