Assistant Professor, University of South Florida
XRDS, 2023
Robots need to be able to communicate with people through natural language. But how should their memory systems be designed to facilitate this communication? Tags: Cognitive robotics, Cognitive science, Natural language generation Introduction As robots become more widely available to the public and play more prominent roles in people’s day-to-day routines, those robots will… Continue reading The Importance of Memory for Language-Capable Robots
INLG 2023
Language-capable robots must be able to efficiently and naturally communicate about objects in the environment. A key part of communication is Referring Form Selection (RFS): the process of selecting a form like it, that, or the N to use when referring to an object. Recent cognitive status-informed computational RFS models have been evaluated in… Continue reading Exploring the Naturalness of Cognitive Status Informed Referring Form Selection Models
CogSci 2023
Robots must be able to communicate naturally and efficiently, e.g., using concise referring forms like it, that, and the ⟨N’⟩. Recently researchers have started working on Referring Form Selection (RFS) machine learning algorithms but only evaluating them offline using traditional metrics like accuracy. In this work, we investigated how a cognitive status-informed RFS computational… Continue reading Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions
IROS 2022
Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots must determine the content and structure of a sequence of utterances, a… Continue reading Givenness Hierarchy Informed Optimal Document Planning for Situated Human-Robot Interaction
INLG 2022
This paper won the Best Long-Paper Award at INLG 2022: For autonomous agents such as robots to effectively communicate with humans, they must be able to refer to different entities in situated contexts. In service of this goal, researchers have recently attempted to model the selection of referring forms on the basis of cognitive… Continue reading Evaluating Referring Form Selection Models in Partially-Known Environments
AAAI-SSS 2022
Autonomous robots must communicate about their decisions to gain trust and acceptance. When doing so, robots must determine which actions are causal, i.e., which directly give rise to the desired outcome, so that these actions can be included in explanations. In behavior learning in psychology, this sort of reasoning during an action sequence has… Continue reading Causal Robot Communication Inspired by Observational Learning Insights
HRI 2022 LBR
In many domains, robots must be able to communicate to humans through natural language. One of the core capabilities needed for task-based natural language communication is the ability to refer to objects, people, and locations. Existing work on robot referring expression generation has focused nearly exclusively on generation of definite descriptions to visible objects.… Continue reading A Task Design for Studying Referring Behaviors for Linguistic HRI
IROS 2020
It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by… Continue reading Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance
