The field of autonomous robotics is experiencing a significant shift towards self-adaptation and dynamic knowledge representation. Researchers are developing innovative frameworks and systems that enable robots to adapt to diverse environments and tasks, and to reason about their actions and decisions. One of the key directions in this field is the development of knowledge-based solutions that can capture and represent complex, context-dependent knowledge. This allows robots to make informed decisions and adapt to new situations, even in the presence of uncertainties. Another important area of research is the development of meta-reasoning frameworks that can enhance robots' decision-making processes in unexpected situations. These frameworks use attention maps and other techniques to reason about reasons and to make decisions in a more flexible and scalable way. Overall, the field is moving towards more autonomous, flexible, and adaptable robots that can operate effectively in a wide range of environments and tasks. Noteworthy papers include: ROSA, a knowledge-based framework for robot self-adaptation, which provides a novel approach to task-and-architecture co-adaptation. KERAIA, an adaptive and explainable framework for dynamic knowledge representation and reasoning, which introduces significant innovations in knowledge representation and reasoning. Meta-reasoning Using Attention Maps and Its Applications in Cloud Robotics, which proposes a revised meta-reasoning framework that improves the scalability of the original approach in unexpected situations.