For robots to seamlessly interact with humans, we first need to make sure that humans and
robots understand one another. Diverse algorithms have been developed to enable robots …

For robots to seamlessly interact with humans, we first need to make sure that humans and
robots understand one another. Diverse algorithms have been developed to enable robots …

To expedite the development process of interactive reinforcement learning (IntRL)
algorithms, prior work often uses perfect oracles as simulated human teachers to furnish …

Task assignment and scheduling algorithms are powerful tools for autonomously
coordinating large teams of robotic or AI agents. However, the decisions these system make …

Preference learning has long been studied in Human-Robot Interaction (HRI) in order to
adapt robot behavior to specific user needs and desires. Typically, human preferences are …

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 guidance is often desired in reinforcement learning to improve the performance of
the learning agent. However, human insights are often mere opinions and educated …

As foundation models increasingly permeate sensitive domains such as healthcare, finance,
and mental health, ensuring their behavior meets desired outcomes and social expectations …

The adoption of Reinforcement Learning (RL) in several human-centred applications
provides robots with autonomous decision-making capabilities and adaptability based on …

Effective multi-agent collaboration is imperative for solving complex, distributed problems. In
this context, two key challenges must be addressed: first, autonomously identifying optimal …