Self-Learning Systems in Hostile-to-Life Environments

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.