Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Deep reinforcement learning has recently seen huge success across multiple areas in the
robotics domain. Owing to the limitations of gathering real-world data, ie, sample inefficiency …

Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during
the last decade to solve various decision-making problems such as autonomous driving …

Path planning is the most fundamental necessity for autonomous mobile robots.
Traditionally, the path planning problem was solved using analytical methods, but these …

Machine learning has become an important research area in many domains and real-world
applications. The prevailing assumption in traditional machine learning techniques, that …

We consider the problem of generalization in reinforcement learning where visual aspects of
the observations might differ, eg when there are different backgrounds or change in contrast …

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

Deep Reinforcement Learning (DRL) methods are increasingly being applied in Unmanned
Underwater Vehicles (UUV) providing adaptive control responses to environmental …

Mapless navigation for Automated Guided Vehicles (AGV) via Deep Reinforcement
Learning (DRL) algorithms has attracted significantly rising attention in recent years …