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Socially Intelligent Machines (SIM) Lab

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.