223 citations · 523 across the 15 of their papers we have counts for
6 papers · 1 filter
Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization
Raphael Trumpp, Denis Hoornaert, Mirco Theile +1
Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic…
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo +1
Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discr…
Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Marco Caccamo +1
Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an efficient path that covers every point in an area of interest. This work addresses the…
Multi-Agent Belief Sharing through Autonomous Hierarchical Multi-Level Clustering
Mirco Theile, Jonathan Ponniah, Or Dantsker +1
Coordination in multi-agent systems is challenging for agile robots such as unmanned aerial vehicles (UAVs), where relative agent positions frequently change due to unconstrained m…
UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Richard Nai +2
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path pl…
UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning
Mirco Theile, Harald Bayerlein, Richard Nai +2
Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control…