As robots move from labs and factories into human-centric spaces, it becomes progressively harder to predetermine the environments, tasks, and human interactions they will need to be able to handle. Letting these 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 the meaning of language as it applies to the physical world. At the same time, physically embodied agents offer a way to learn to understand natural language in the context of the world to which it refers. In this presentation, I will give an overview of our work on joint statistical models to learn the grounded semantics of natural language describing objects, spaces, and actions, and present recent work on using simulation-to-reality approaches to learn from unconstrained human-robot interactions.
Jul 29, 2020 2:00 PM — 3:00 PM
BioDr. Cynthia Matuszek is an assistant professor of computer science and electrical engineering at the University of Maryland, Baltimore County. Dr. Matuszek directs UMBC’s Interactive Robotics and Language lab, in which research is focused on robots’ acquisition of grounded language, including work in human-robot interfaces, natural language, machine learning, and collaborative robot learning. She has developed a number of algorithms and approaches that make it possible for robots to learn about their environment and how to follow instructions from interactions with non-technical end users. She received her Ph.D. in computer science and engineering from the University of Washington in 2014. Dr Matuszek has published in artificial intelligence, robotics, and human-robot interaction venues, and was named in the most recent IEEE bi-annual “10 to watch in AI.”