Graduate Research Talk: Coordination through Dialogue: Applications in Human-Robot Teaming

Abstract

Robots are increasingly needed to serve on teams with people in some of the most complex domains. Since human teams often rely on task-oriented dialogue to coordinate their actions, robot teammates will no doubt need to rely similarly on this modality. However, constraining factors such as workload, time pressure, and reduced situational awareness are prevalent in these domains, and affect human speech through increased speech rate, disfluency, and speech overlap. Though typically viewed as "messy" features of speech that are either ignored or parsed out of most dialogue systems, evidence from Psycholinguistics and Conversation Analysis suggests that these features are important resources in the interaction that can support coordination. Thus, in order for robots to serve as effective teammates, they will need to be able to handle (and in some cases, produce) these features of natural language. In this talk, I will give an overview of my work to date addressing this challenge.

First, I will discuss some empirical work using the Cooperative Remote Search Task (CReST) corpus to identify dialogue and interaction patterns in a search-and-rescue scenario involving human teams. I will then show some of the novel mechanisms we have implemented in a cognitive robotic architecture to enable robots to handle some of the features seen in the corpus - in particular, overlapping speech. Finally, I will discuss remaining work, including additional modelling as well as evaluation scenarios for testing our proposed mechanisms and for further studying human-robot teaming.