Recent research in robot learning suggests that implicit human feedback is a low-cost
approach to improving robot behavior without the typical teaching burden on users. Because …

Recent work in Human-Robot Interaction (HRI) has shown that robots can leverage implicit
communicative signals from users to understand how they are being perceived during …

Non-verbal behavior is a central challenge in understanding the dynamics of a conversation
and the affective states between interlocutors arising from the interaction. Although …

My work aims to enable robots to more effectively learn how to help people. The way in
which people want to be helped by robots can vary by task, person, or time, among other …

Human impressions of robot performance are often measured through surveys. As a more
scalable and cost-effective alternative, we study the possibility of predicting people's …

Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-
tune or train agentic machine learning models. Similar to how humans interact in social …

Effective collaboration between humans and AIs hinges on transparent communication and
alignment of mental models. However, explicit, verbal communication is not always feasible …

My work aims to create interactive agents that are more effectively able to help people. The
way in which people want to be helped can vary based on a number of factors, such as …

Human impressions of robot performance are often measured through surveys. As a more
scalable and cost-effective alternative, we investigate the possibility of predicting people's …

Robots that carry out tasks and interact in complex environments will inevitably commit
errors. Error detection is thus an essential ability for robots to master to work efficiently and …