Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
An end-to-end deep neural network for autonomous driving designed for embedded automotive platforms
In this paper, one solution for an end-to-end deep neural network for autonomous driving is
presented. The main objective of our work was to achieve autonomous driving with a light …
presented. The main objective of our work was to achieve autonomous driving with a light …
A brief survey: Deep reinforcement learning in mobile robot navigation
Conventional navigation techniques have mainly relied on a global information approach,
wherein pre-built laser or camera environment maps are used to construct a path from a …
wherein pre-built laser or camera environment maps are used to construct a path from a …
Convergence of machine learning and robotics communication in collaborative assembly: mobility, connectivity and future perspectives
Collaborative assemblies of robots are promising the next generation of robot applications
by ensuring that safe and reliable robots work collectively toward a common goal. To …
by ensuring that safe and reliable robots work collectively toward a common goal. To …
Towards monocular vision-based autonomous flight through deep reinforcement learning
This paper proposes an obstacle avoidance strategy for small multi-rotor drones with a
monocular camera using deep reinforcement learning. The proposed method is composed …
monocular camera using deep reinforcement learning. The proposed method is composed …
Deepracer: Autonomous racing platform for experimentation with sim2real reinforcement learning
DeepRacer is a platform for end-to-end experimentation with RL and can be used to
systematically investigate the key challenges in developing intelligent control systems …
systematically investigate the key challenges in developing intelligent control systems …
Deepracer: Educational autonomous racing platform for experimentation with sim2real reinforcement learning
DeepRacer is a platform for end-to-end experimentation with RL and can be used to
systematically investigate the key challenges in developing intelligent control systems …
systematically investigate the key challenges in developing intelligent control systems …
Learn to navigate autonomously through deep reinforcement learning
In this article, we propose a deep reinforcement learning (DRL) algorithm as well as a novel
tailored neural network architecture for mobile robots to learn navigation policies …
tailored neural network architecture for mobile robots to learn navigation policies …
Achieving real-time path planning in unknown environments through deep neural networks
Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation.
In this paper, we propose a novel deep neural network (DNN) based method for real-time …
In this paper, we propose a novel deep neural network (DNN) based method for real-time …
Multimodal deep reinforcement learning with auxiliary task for obstacle avoidance of indoor mobile robot
It is an essential capability of indoor mobile robots to avoid various kinds of obstacles.
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …