Learning Cooperative Personalized Policies from Gaze Data

An ideal Mixed Reality (MR) system would only present virtual information (e.g., a label) when it is useful to the person. However, deciding when a label is useful is challenging: it depends on a variety of factors, including the current task, previous knowledge, context, etc. In this paper, we propose a Reinforcement Learning (RL) method to learn when to show or hide an object's label given eye movement data. We demonstrate the capabilities of this approach by showing that an intelligent agent can learn cooperative policies that better support users in a visual search task than manually designed heuristics. Furthermore, we show the applicability of our approach to more realistic environments and use cases (e.g., grocery shopping). By posing MR object labeling as a model-free RL problem, we can learn policies implicitly by observing users' behavior without requiring a visual search model or data annotation.