Objective: Robots provide artificial agents a presence in the physical world and a unique opportunity to impact it directly. These robots face unique challenges, as they will be exposed to completely unfamiliar surroundings and situations. The Robot Learning Lab seeks to advance the foundations of robot perception, state estimation and planning using learning approaches to enable robots to reliably operate in more complex domains and diverse environments. Our focus is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive using a diverse set of modalities and experience by interacting with the real-world.

Research Areas: Perception, State Estimation, Motion Planning, Mobile Manipulation, Human-Robot Interaction, Learning Fundamentals, Responsible Robots