Fotos: Sandra Meyndt
The center is pleased and proud that the two have chosen Freiburg despite attractive offers from other universities. "The University of Freiburg, its Faculty of Engineering as well as the Center BrainLinks-BrainTools offer me optimal conditions for my research," says Abhinav Valada, who with his professorship in Robot Learning succeeds Wolfram Burgard. "Also in view of the ongoing second round of the Excellence Strategy of the German federal and state governments, the decision for Freiburg was quickly clear for me. With “BrainWorlds - Internal World Models in Animals, Humans and Artificial Intelligence”, we are involved in an outstanding excellence cluster initiative to which we can contribute in a decisive way with our research," adds Joschka Bödecker, whose professorship is dedicated to neurorobotics.
Robot Learning Lab
Abhinav Valada received his PhD in Computer Science from the Chair of Autonomous Intelligent Systems, Institute of Computer Science, University of Freiburg. Since 2019 he is Junior Professor and Head of the Robot Learning Lab, Institute of Computer Science, University of Freiburg.
Valada and his group seek to advance the foundations of robot perception, state estimation and planning using learning approaches to enable robots to reliably operate in more complex domains and diverse environments. Their focus is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive using a diverse set of modalities and experience by interacting with the real-world.
Neurorobotics Lab
Joschka Bödecker received his PhD from the Chair of Engineering, Machine Learning, Robotics at Osaka University. From 2015 to 2017, he deputized for Prof. Martin Riedmiller and led the Machine Learning Lab at the University of Freiburg. Since fall 2017, he is Junior Professor at Neurorobotics Lab, Institute of Computer Science, University of Freiburg.
Bödecker’s Neurorobotics Lab develops machine learning techniques for robotics and neurotechnology. They focus on data-efficient deep reinforcement learning methods that provide robust and adaptive solutions, and target applications on real-world devices.
https://nr.informatik.uni-freiburg.de