Advanced mobile robot systems need to accomplish increasingly complex task sets. However, to solve demanding problems, they are typically optimized to a very restricted set of tasks and environments. This work will therefore propose a self-reconfigurable software and hardware architecture to allow the dynamic optimization of a robot system depending on the current situation, i. e. the current task, the robot inner state, and the environment. The proposed framework is based on organic computing principles and unsupervised machine learning techniques. It further uses dynamically reconfigurable Field Programmable Gate Arrays (FPGA) as hardware accelerators. Preliminary results will be presented, which demonstrate the feasibility of the self-reconfiguration approach.