Kalesha Bullard, Georgia Tech
Abstract: When a robotic agent is given a recipe for a task, it must perceptually ground each entity and concept within the recipe (e.g., items, locations) in order to perform the task. Assuming no prior knowledge, this is particularly challenging in newly situated or dynamic environments, where the robot has limited representative training data. This research examines the problem of enabling a social robotic agent to leverage interaction with a human partner for learning to efficiently ground task-relevant concepts in its situated environment. Our prior work has investigated Learning from Demonstration approaches for the acquisition of (1) training instances as examples of task-relevant concepts and (2) informative features for appropriately representing and discriminating between task-relevant concepts. In ongoing work, we examine the design of algorithms that enable the social robot learner to autonomously manage the interaction with its human partner, towards actively gathering both instance and feature information for learning the concept groundings. This is motivated by the way that humans learn, by combining information rather than simply focusing on one type. In this talk, I present insights and findings from our initial work on learning from demonstration for grounding of task-relevant concepts and ongoing work on interaction management to improve the learning of grounded concepts.
Bio: Kalesha Bullard is a PhD candidate in Computer Science at Georgia Institute of Technology. Her thesis research lies at the intersection of Human-Robot Interaction and Machine Learning: enabling a social robot to learn groundings for task-relevant concepts, through leveraging and managing interaction with a human teacher. She is co-advised by Sonia Chernova, associate professor in the school of Interactive Computing at Georgia Tech, and Andrea L. Thomaz, associate professor in the department of Electrical and Computer Engineering at The University of Texas in Austin. Before coming to Georgia Tech, Kalesha received her undergraduate degree in Mathematics Education from The University of Georgia and subsequently participated in the Teach For America national service corps as a high school mathematics teacher. Over the course of her research career, Kalesha has served as a Program Committee co-chair for three different workshops and symposia, completed research internships at IBM Watson and NASA Jet Propulsion Laboratory, and was awarded an NSF Graduate Research Fellowship and a Google Generation Scholarship. Kalesha’s broader personal research vision is to enable social robots with the cognitive reasoning abilities and social intelligence necessary to engage in meaningful dialogue with their human partners, over long-term time horizons. Towards that end, she is particularly interested in grounded and embodied dialogue whereby the agent can communicate autonomously, intuitively, and expressively.