The vision of our research is to enable robots to function in dynamic human environments by allowing them to flexibly adapt their skill set via learning interactions with end-users. We call this Socially Guided Machine Learning (SG-ML), exploring the ways in which Machine Learning agents can exploit principles of human social learning. To date, our work in SG-ML has focused on two research thrusts: (1) Interactive Machine Learning, and (2) Natural Interaction Patterns for HRI. Here you will find recent examples of projects in each of these two thrusts.