Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has
proven to be very successful in the recent years. However, most of the publications focus …

We use reinforcement learning (RL) to demonstrate an easily reproducible setup to learn
dynamic obstacle avoidance for a robotic arm based on sensory input as it follows a pre-…

… In order to comply with the fast expanding spectrum of anaesthesiological services, Zindler
organized regular lectures which were given twice weekly. In addition, he established a …

This book presents the proceedings of the 31st International Conference on Robotics in Alpe-Adria-Danube
Region (RAAD), held in Klagenfurt, Austria, June 8-10, 2022. It gathers …

Many studies utilize reinforcement learning in simulation environments to control robots. Since
simulation environments do not provide reinforcement learning environments for all robots…

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS)
susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations …

The COVID-19 pandemic confronts governments and their health systems with great
challenges for disease management. In many countries, hospitalization and in particular ICU …

The asthenosphere beneath the global network of spreading ridges is continuously sampled
by partial melting, generating mid-ocean ridge basalts (MORB). Zero-age basalts and …

In this paper, we use standard scenarios focussing on renewable energy, energy efficiency
and grid investments. We take stock of the literature and quantitative data on available …

Robotic systems are more connected, networked, and distributed than ever. New architectures
that comply with the de facto robotics middleware standard, ROS 2, have recently …