ABSTRACT: Achieving a high level of autonomy of robots operating in extreme environments is particularly desirable but also particularly challenging due to  uncertain and potentially varying operating conditions. By extreme environments we mean remote or hardly accessible environments where robots need to rely largely on local limited resources for their control implementation as for example underwater robots for collecting litter in marine environments. In this scenario the strong influence of nonlinear hydrodynamics on the motion of underwater robots and (often unpredictable) influences like currents as well as the distorted perception of the environment pose significant challenges for precise control and safe operation. Recent progress in machine learning for control promises high performance in such uncertain conditions, yet many of the available approaches cannot directly be applied due to the limited available resources in terms of local computational power and communication. Hence apart from the challenge of providing safety and performance guarantees for learning control, also the efficient implementation plays an important role.

In this talk we will present results on learning-based control with performance guarantees for nonlinear systems in uncertain environment and under resource constraints on the example of an underwater robotic system with manipulation capabilities. We will introduce approaches to evaluate data-efficiency in non-parametric modeling techniques and show that the control task matters in this respect.  The promises of physics-informed learning techniques to improve learning performance in terms of data efficiency and under noisy training conditions will be discussed. Furthermore, different approaches to achieve realtime performance of non-parametric machine learning techniques given limited resources will be presented. While the proposed approaches promise to bring us a step further towards implementable high performance control for robots in extreme environments we will also discuss the remaining challenges as well as their limits.