An HRI Approach to Feature Selection

Our research seeks to enable social robots to ask intelligent questions when learning tasks from human teachers. We use the paradigm of Learning from Demonstration (LfD) to address the problem of efficient learning of task policies by example (Chernova and Thomaz 2014). In this work, we explore how to leverage human domain knowledge for task model construction, by allowing users to directly select a set of the salient features for classification of objects used in the task being demonstrated.