Research Areas
URIL is dedicated to advancing the development of intelligent robots that can be tailored to diverse end-user needs. To achieve this vision, our research focuses on two key areas:
Improving Robot Skill Learning
We aim to improve the efficiency and adaptability of robots by enabling few-shot and
zero-shot learning capabilities. Our approach includes developing structured learning
frameworks such as novel action representations that facilitate efficient learning, and
intermediate representations of motion that bridge visual representation and low-level
motions.
Advancing Human Modeling
We draw inspiration from human interaction with the physical world and other agents to
design learning algorithms that improve human-robot collaboration.
Our efforts focus on designing algorithms that mirror how humans interact with the
environment, and
leverage multimodal human cues for human-robot interaction, in both shared and full autonomy
settings.