Using Exploratory Search to Learn Representations for Human Preferences

Robots that interact with humans must adapt to the different preferences of human users. However, the time and effort needed for non-expert users to specify their preferences to a robot are a barrier to effective robot adaptation. Better representations of user preferences in the form of learned features have the potential to facilitate robot adaptation. In this work, we propose a method to learn representations using Contrastive Learning from Exploratory Actions (CLEA) that leverages data automatically collected from an interactive signal design processes to better learn user preferences. We show that using data collected automatically from the design process can aid with learning user preferences compared to the alternative of purely self-supervised learning.