There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans. This includes autonomous vehicles that interact with human-driven vehicles or pedestrians, service robots collaborating with their users at homes over short or long periods of time, or assistive robots helping patients with disabilities. This introduces an opportunity for developing new robot learning algorithms that can help advance interactive autonomy.

In this talk, we will discuss a formalism for human-robot interaction built upon ideas from representation learning. Specifically, we will first discuss the notion of latent strategies — low dimensional representations sufficient for capturing non-stationary interactions. We will then talk about the challenges of learning such representations when interacting with humans, and how we can develop data-efficient techniques that enable actively learning computational models of human behavior from demonstrations and preferences.