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 …

Despite some successful applications of goal-driven navigation, existing deep reinforcement
learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of …

We present a hierarchical deep reinforcement learning (DRL) framework with prominent
sampling efficiency and sim-to-real transfer ability for fast and safe navigation: the low-level …

Mapless navigation is a popular approach for guiding a robot in an unknown environment.
However, current learning-based methods for mapless navigation cannot guarantee safe …

The multimodal perception of intelligent robots is essential for achieving collision-free and
efficient navigation. Autonomous navigation is enormously challenging when perception is …

With the development of learning-based autonomous underwater exploration method of
robotic fish, how to improve data quality and sampling efficiency, so as to achieve better …

Aiming at the autonomous navigation of unmanned aerial vehicles (UAVs) in complex and
unknown environments, this article combines transfer reinforcement learning theory with an …

Reinforcement learning (RL) requires a lot of interactions with the environment, which is
usually expensive or dangerous in real-world tasks. To address this problem, offline RL …

The ability of autonomous undersea vehicles (AUVs) to plan paths in unknown marine
environments is the precondition for executing complicated missions. However, existing path …

Deep reinforcement learning (DRL) is widely applied in motion planning for multi-robot
systems as DRL leverages the offline training process to improve the real-time computation …