College students often struggle to balance their work with personal wellness. In part, this occurs because students work when they are unable to focus. We hypothesize that we can adapt the Experience Sampling Method (ESM) to build a model of users’ efficacy and predict when they will be most likely to experience flow, a state of motivation and immersion. We also hypothesize that we can present this information effectively to users, allowing them to understand when they are most likely to achieve flow. In order to test these hypotheses, we introduce the Productivity and Wellness Pal (PaWPal), a smartphone-based application that seeks to make users aware of their efficacy at various tasks as well as which courses of action are likely to lead to immersive experiences.

As human-robot teamwork becomes increasingly common, a key challenge is to fluidly and intuitively coordinate team members' interactions. In this work, we explore two modalities of human-robot coordination: active, where agents intentionally attempt to understand and influence the plans of human teammates, and passive, where agents simply react to their human teammates' varying behavior. In our Productivity and Wellness Pal (PaWPal) project, we seek to develop an agent that actively elicits a teammate's constraints, preferences, and goals in order to nudge them towards better behavior. Conversely, in our Coordinating Human-Robot Teamwork project, we take a distributed approach to scheduling where agents passively adapt to teammates' plan executions. Our research hypothesis is that human-robot coordination Figure 1: Screenshots from our ESM study, conducted using techniques will lead to more natural and effective PACO on Android (http://www.pacoapp.com/).