Christian Jestel

• Fraunhofer Institute for Material Flow and Logistics

Autonomous Mobile Robots (AMRs) continue to facilitate the work of physicians and hospital nurses by releasing those professionals from time consuming transport tasks within hospitals. Nonetheless, AMRs still often face challenges when situations occur, which result in a failure of the navigation system. In this paper, we present an analysis and an...

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its application to the real-world robotics domain. Guiding the learning process with additional knowledge offers a potential solution, thus leveraging the strengths of data- and knowledge-driven approaches. However, this field of research encompasses several d...

Multi-robot navigation is a challenging task in which multiple robots must be coordinated simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) to learn a decentralized end-to-end policy which maps raw sensor data to the command velocities of the agent. In order to enable the policy to generalize, the training is pe...

Deep Reinforcement Learning has been successfully applied in various computer games [8]. However, it is still rarely used in real-world applications, especially for the navigation and continuous control of real mobile robots [13]. Previous approaches lack safety and robustness and/or need a structured environment. In this paper we present our proof...