Reliable and stable locomotion has been one of the most fundamental challenges for
legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method …

Our goal is to investigate whether discrete state space algorithms are a viable solution to
continuous alternatives for mapless navigation. To this end we present an approach based …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …

Reliable real-time planning for robots is essential in today's rapidly expanding automated
ecosystem. In such environments, traditional methods that plan by relaxing constraints …

Over the past decade, unmanned aerial vehicles (UAVs) have been widely applied in many
areas, such as goods delivery, disaster monitoring, search and rescue etc. In most of these …

We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle
avoidance, and formation control of nonholonomic robots. By separating vision-based …

Learned Neural Network based policies have shown promising results for robot navigation.
However, most of these approaches fall short of being used on a real robot due to the …

Reinforcement learning (RL) has shown to be a valuable tool in training neural networks for
autonomous motion planning. The application of RL to a specific problem is dependent on a …

In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising method
for robot collision avoidance. However, such DRL models often come with limitations, such …

Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural
network architectures. We propose a novel approach to automatically find strong topologies …