Human-Driven Feature Selection for a Robotic Agent Learning Classification Tasks from Demonstration

The state features available to a robot define the variables on which the learning computation depends. However, little prior work considers feature selection in the context of deploying a general-purpose robot able to learn new tasks. In this work, we explore human-driven feature selection in which a robotic agent can identify useful features with the aid of a human user, by extracting information from users about which features are most informative for discriminating between classes of objects needed for a given task (e.g. sorting groceries). The research questions examine (a) whether a domain expert is able to identify a subset of informative task features, (b) whether human selected features will enable the agent to classify unseen examples as accurately as using computational feature selection, and (c) if the interaction strategy used to elicit the information from the user impacts the quality of resulting feature selection. Toward that end, we conducted a user study with 30 participants on campus, given a multi-class classification task and one of five different approaches for conveying information about informative features to a robot learner. Our findings show that when features are semantically interpretable, human feature selection is effective in LfD scenarios because it is able to outperform computational methods when there is limited training data, yet still remains on-par with computational methods as the training sample size increases.