The Speakers

Prof. Cynthia Matuszek

Language as a Mechanism for Interactive Robot Learning: Promise and Perils

Video recording: https://youtu.be/OLJkTPk6n94

Abstract: As robots move into human-centric spaces, it becomes progressively less feasible to try to predetermine the kinds of tasks and goals they will need to handle, and allowing them to learn about requirements from direct interaction with end users becomes more critical. Letting robots learn from natural language interactions is an intuitive, versatile approach to handling robot learning about novel contexts, but learning from language remains a large-scale unsolved problem. In this talk, I will describe some of the key challenges to advancing learning from language, such as: How can robots take advantage of physical context to learn more efficiently from language? What disparities arise in machine learning systems that deal with different kinds of people? And, how can we safely incorporate large language models? Although we don't know the answers to all of these questions, I will discuss promising avenues of research that are currently seeking to address some of the problems raised. This includes learning from language as a grounded, contextual problem, understanding how and whether that context introduces biases, and—especially as LLMs are rapidly being incorporated into a variety of systems—understanding the context that those models carry with them and discussing some of the risks as well as the rewards of those systems.

Bio: Cynthia Matuszek is an associate professor of computer science and electrical engineering at the University of Maryland, Baltimore County, and the director and founder of UMBC’s Interactive Robotics and Language lab. She holds a Ph.D. from the University of Washington. 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, and machine learning, informed by a background in common-sense reasoning and classical artificial intelligence. Dr. Matuszek has published in machine learning, artificial intelligence, robotics, and human-robot interaction venues. She was named one of IEEE Spectrum’s 10 to Watch in AI and has received an NSF career award as well as a UMBC early faculty achievement award.

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Prof. Stephanie Tellex

Towards Complex Language in Partially Observed Environments

Video recording: https://youtu.be/iGI-WWNOL94

Abstract: Robots can act as a force multiplier for people, whether a robot

assisting an astronaut with a repair on the International Space

station, a UAV taking flight over our cities, or an autonomous vehicle

driving through our streets. Existing approaches use action-based

representations that do not capture the goal-based meaning of a

language expression and do not generalize to partially observed

environments. The aim of my research program is to create autonomous

robots that can understand complex goal-based commands and execute

those commands in partially observed, dynamic environments. I will

describe demonstrations of object-search in a POMDP setting with

information about object locations provided by language, and mapping

between English and Linear Temporal Logic, enabling a robot to

understand complex natural language commands in city-scale

environments. These advances represent steps toward robots that

interpret complex natural language commands in partially observed

environments using a decision theoretic framework.

Bio: Stefanie Tellex is an Associate Professor of Computer Science at

Brown University. Her group, the Humans To Robots Lab, creates robots

that seamlessly collaborate with people to meet their needs using

language, gesture, and probabilistic inference, aiming to empower

every person with a collaborative robot. She completed her Ph.D. at

the MIT Media Lab in 2010, where she developed models for the meanings

of spatial prepositions and motion verbs. Her postdoctoral work at

MIT CSAIL focused on creating robots that understand natural language.

She has published at SIGIR, HRI, RSS, AAAI, IROS, ICAPs and ICMI,

winning Best Student Paper at SIGIR and ICMI, Best Paper at RSS, and

an award from the CCC Blue Sky Ideas Initiative. Her awards include

being named one of IEEE Spectrum's AI's 10 to Watch in 2013, the

Richard B. Salomon Faculty Research Award at Brown University, a DARPA

Young Faculty Award in 2015, a NASA Early Career Award in 2016, a 2016

Sloan Research Fellowship, and an NSF Career Award in 2017. Her work

has been featured in the press on National Public Radio, BBC, MIT

Technology Review, Wired and Wired UK, as well as the New Yorker. She

was named one of Wired UK's Women Who Changed Science In 2015 and

listed as one of MIT Technology Review's Ten Breakthrough Technologies

in 2016.

Website: http://h2r.cs.brown.edu/

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