~~NOTOC~~
=====Prof. Dr. Daniel Leidner=====
{{:wiki:dleidner.jpg?0x180}} 

As research group leader, Daniel is working on the service robots of tomorrow. His methods from the field of artificial intelligence are already today pioneering in the robotic exploration of Mars. As collaborator in EASE, he aims to apply the same methods to master everyday activities. With this, he is pursuing the vision that future robots should no longer be mere tools but in fact intelligent colleagues and assistants for space and society.

=====Adrian Bauer=====
{{:wiki:bauer_adrian.jpg?0x180}}
 
Adrian is elaborating a PhD thesis on physics-based probabilistic state inference for fault-tolerant planning. For this, he develops a multi-physics simulation framework, that allows mirroring a robot’s operation and tracking of multiple possible outcome possibilities at once. Using this information, the robot shall be enabled to make crucial decisions circumvent error prone situations.

=====Katharina Hagmann=====
{{:wiki:hagmann_katharina.jpg?0x180}} 

Katharina’s PhD topic is formulated around the important task of supporting remote operators though cognitive assistance functions. With this, she develops methods that run in synergy with the circumvention of failure states. She focuses on assistance functions for operators using physics-based simulation as it is required for the overall goal of the group.

=====FUTURO – Failure and Uncertainty Tolerant Universal Robot Operation=====
{{:wiki:futuro_logo.png?0x120}}

The FUTURO Young Investigator Group is funded by the DLR Management Board Young Research Group Leader Program and the Executive Board Member for Space Research and Technology. 

The FUTURO Young Investigator Group intends to support upcoming space exploration missions, including technology demonstration experiments that involve robots controlled from the International Space Station (ISS) as well as astronaut-robot collaboration missions aiming for Moon and Mars. The overall goal of the research conducted by the group is thus to advance the autonomy of future space assistance robots. 

The group aims to utilize AI-based planning, develop probabilistic physics reasoning methods, and leverage modern machine learning techniques to handle failure situations and abstract new skills from human demonstration likewise. However, it is not straightforward to interpret robot motions and their effects to the environment in a meaningful way. Probabilistic effects have to be considered, and the data has to be semantically interpreted. To minimize the overhead, the FUTURO group exploits existing synergies to develop an integrated probabilistic reasoning and inference framework that can be used for both failure handling and skill abstraction.

In view of demographic change as one of the most urgent challenge for our society, developments to increase fault tolerance and the acquisition of robotic skills should not only be used in space exploration in the long term, but also be of benefit to the general public. This links the FUTURO Group to the EASE CRC https://ease-crc.org/ , which aims to master everyday manipulation tasks. Forming an interconnected research group, the two projects work together towards the declared goal of making service robots ready for the challenges of everyday activities.