This study extends the learning and use of affordances on robots on two fronts. First, we use the very same affordance learning framework that was used for learning the affordances of inanimate things to learn social affordances, that is affordances whose existence requires the presence of humans. Second, we use the learned affordances for making multi-step plans. Specifically, an iCub humanoid platform is equipped with a perceptual system to sense objects placed on a table, as well as the presence and state of humans in the environment, and a behavioral repertoire that consisted of simple object manipulations as well as voice behaviors that are uttered simple verbs. After interacting with objects and humans, the robot learns a set of affordances with which it can make multistep plans towards achieving a demonstrated goal.