Learning Vision-based Navigation Policies for Information Gathering with Quadcopters: A Deep Reinforcement Learning Approach

Deep reinforcement learning presents a compelling approach for the exploration of cluttered 3D environments, offering a balance between fast computation and effective vision-based navigation. Yet, the use of 3D navigation for learning-based information gathering remains largely unexplored. Navigation in 3D space poses the challenge of having an...

Learning to search & track dynamic targets with graph representations

Autonomous robots have been widely applied to search and rescue missions for information gathering about target locations. This process needs to be continuously replanned based on new observations in the environment. For dynamic targets, the robot needs to not only discover them but also keep tracking their positions. Previous works focus on...

Learning a guidance policy to navigate among dynamic agents in constrained environments with continual reinforcement learning

Mobile robots that operate in human environments require the ability to safely navigate among humans and other obstacles. Existing approaches use Deep Reinforcement Learning (DRL) to obtain safe robot behavior in such environments, but do not ensure collision avoidance or trajectory feasibility. This issue is solved by methods combining DRL with...