*This was a HYBRID Event with in-person attendance in Wu & Chen Auditorium and Virtual attendance via Zoom Webinar
There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when studying how robot learning can advance interactive agents such as robots that collaborate with humans, and how interactions can enable more effective robot learning. This introduces an opportunity for developing new robot learning algorithms that can help advance the science of interactive autonomy. In this talk, we will discuss a formalism that learns conventions, i.e., low-dimensional representations sufficient for capturing non-stationary interactions. We demonstrate how we can influence and stabilize these conventions to achieve desirable outcomes in multi-robot coordination. Finally, we will then talk about some of the challenges of learning such representations when interacting with humans, and how we can develop data-efficient techniques that can tap into different sources of data such as suboptimal demonstrations or can actively learn human preferences. We will end the talk with a discussion of applications of these techniques in assistive robotics.