In this paper, we describe an Ambient Assisted Living (AAL) scenario which includes a robotic platform employed in automatic data collection and assistance. As part of this scenario we propose new algorithms and solutions for indoor localization and manipulation. The former involves automatic data collection of WLAN RSS fingerprints by the robotic platform. The latter involves object manipulation with focus on grasping algorithms. Our new hybrid approach, where the localization problem is split into two stages, which are modelled as a classification and regression problem, proves to deliver the best positioning accuracy even when compared with deterministic approaches. At the same time, we also obtained improved results in the object grasping approach. The improvement is most pronounced for previously unseen data showing that our method is able to better generalize new environments.