Abstract: Societal facts like the increase in life expectancy, the lack of nursing staff, the hectic rhythms of everyday life, and the recent unprecedented situation of the Covid-19 pandemic, make the need for intelligent robotic assistants more urgent than ever. Spanning their applications from home-environments to hospitals, workhouses to agricultural development, etc., the embodied AI robotic assistants are in the epicenter of modern robotics and AI research. In this talk, I will go through my research work for developing intelligent methods for such assistive agents. We will draw the big picture of intelligent service robots, and we will specifically focus on sub-problems that I have tackled in the last few years. The main research areas we will cover consider: the perception and recognition of human activities, combining classical methods like tracking with machine learning for extracting useful multi-sensor human-related information for robot action planning and control; algorithms for encoding object-features that allow 6D tracking and grasp-planning; we will discuss methods that can leverage human-centered information for learning intelligent robot behavior using reinforcement learning; and, I will elaborate on our recent work about accelerated policy learning of manipulation tasks both through the effective combination of imitation and reinforcement learning, but also through a novel method for model-predictive policy optimization. While these topics cover only partial aspects of the bigger problem, we will discuss the open research questions on the combination of learning, reasoning, and planning in unstructured environments using mobile manipulator robots. Mobile manipulators are the most emblematic systems to encapsulate the benefits of embodied AI research towards achieving the long-term vision of developing intelligent robotic assistants.