WG 7: Hostile-to-Life Environments
No matter if in the deep sea, in outer space, in contaminated areas or in crisis regions: Self-learning systems can operate at locations which are dangerous, intolerable or harmful for humans. Assisting systems and robots have different levels of automation and autonomy depending on the operation site and their mission. At the moment, they are still highly dependent on human specifications. Eventually, robots will be able to solve complex tasks together with humans - and they will operate and move in unknown territory on their own. Considering these new technological developments, new questions arise: What new business models emerge? And which juridical and ethical challenges must be tackled?
Topics and Organisation of the Working Group
The working group focuses on the requirements and technologies for the application of self-learning systems in difficult-to-access and dangerous environments. It also addresses issues related to the transparency of such systems and the decision-making power of human individuals.
Members of the Working Group
- Prof. Dr.-Ing. Alin Olimpiu Albu-Schäffer
- German Aerospace Center (DLR)
- Prof. Dr.-Ing. Tamim Asfour
- Karlsruhe Institute of Technology (KIT)
- Prof. Dr. Sven Behnke
- University of Bonn
- Prof. Dr. Andreas Birk
- Jacobs University Bremen
- Prof. Dr. rer. Nat. Dipl.-Ing. Thomas Deserno
- Technische Universität Braunschweig
- Dr.-Ing. Jeronimo Dzaack
- ATLAS ELEKTRONIK GmbH
- Dr. Thomas Egloffstein
- ICP Ingenieurgesellschaft mbH
- Dr. -Ing. Michael Gustmann
- Kerntechnische Hilfsdienst GmbH
- Prof. Dr. Andreas Nüchter
- Universität Würzburg
- Dr.-Ing. Hauke Speth
- Institut der Feuerwehr NRW
- Dr. Sirko Straube
- German Research Center for Artificial Intelligence (DFKI)
- Dr.-Ing. Igor Tchouchenkov
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB
- Martin Zimmermann
- imsimity GmbH
We have received written consent from the listed persons for the publication of their data in accordance with the DSGVO. This list of members is an excerpt and will be completed continuously.
Key Questions for the Working Group
- Which avail do self-learning systems have in hostile-to-life environments for individuals and society?
- How does adaptation and compensation for function- and subsystems failures work in those environments?
- How does autonomous learning succeed in long-term remote (autonomous) systems?
- How can the systems learn from sparse data in unique situations (keyword: incremental learning)?
- How can skills and generalized learning (inductive learning) be transferred?
- Which challenges can occur when AI is used against humans (keyword: dual-use)?
Conclusions and Contributions of the Working Group
A comprehensive overview of all results and contributions of the working group available in German can be found here.
- Self-Learning Systems in Hostile-to-Life Environments
- Year of publication: 2019
- Whether exploring terrain that is difficult to access or carrying out rescue operations, the use of Artificial Intelligence (AI) promises to provide helpful solutions in hostile-to-life environments. Mobile robots and assistance systems that adapt to changed situations without needing to be re-programmed can effectively support human beings with activities in hazardous environments. These activities could include fires, disaster control and deep-sea maintenance work, for example. Self-learning systems like these reduce risks for the personnel involved, speed up responses in time-critical situations and plug capability shortfalls in situations where humans are not yet able to respond adequately. In doing so, they deliver valuable social benefits. At the same time, AI-based systems offer huge potential for research and the economy. Thanks to their ability to learn, they make operations in hazardous or poorly accessible environments much more cost-effective than manned missions – or even possible in the first place. There are, however, still a number of technical challenges that need to be met when it comes to deploying self-learning systems in hostile-to-life environments. These include ensuring long-term autonomy and autonomous learning in unknown environments. Another important area involves shaping the interaction and cooperation between autonomous robots or assistance systems and people.
- Contact: Birgit Obermeier / Linda Treugut
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Coordination of the Working Group at the Managing Office: Maximilian Hösl