Approaches to Grounded Language Acquisition from Human Interaction

Location:

BBB3725/ Zoom (Zoom link; Password if needed: MichiganAI)

Abstract:

As robots move from labs and factories into human-centric spaces, it becomes progressively harder to predetermine the environments and interactions they will need to be able to handle. Letting robots learn from end users via natural language is an intuitive, versatile approach to handling novel situations robustly. Grounded language acquisition is concerned with learning to understand language in the context of the physical world. In this presentation, I will give an overview of our work on using joint statistical models to learn the grounded semantics of natural language describing an agent’s environment, and will describe work on applying those models in a sim-to-real language learning environment. I will also discuss the role of speech understanding in grounded language learning, including introducing a new dataset and results on learning directly from that speech.

Bio:

Cynthia Matuszek is an assistant professor of computer science and electrical engineering at the University of Maryland, Baltimore County, and the director of UMBC’s Interactive Robotics and Language lab. After working as a researcher on the Cyc project, she received her Ph.D. in computer science and engineering from the University of Washington in 2014. Her research is focused on how robots can learn grounded language from interactions with non-specialists, which includes work in not only robotics, but human-robot interactions, natural language, machine learning, machine bias, and collaborative robot learning, informed by a background in common-sense reasoning and classical artificial intelligence. Dr Matuszek has been named in the IEEE bi-annual “10 to watch in AI,” and has published in machine learning, artificial intelligence, robotics, and human-robot interaction venues.