Towards Genuine Robot Teammates: Inferring and Applying Cognitive State to Enable Novel Human-Autonomy Teaming Capabilities
Author(s)
Kolb, Jack
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
What makes a teammate? Surprisingly little research has focused on developing robot capabilities that replicate the interactions and inferences commonly observed in human-human teams. This dissertation investigates a broad range of interaction techniques and application domains that leverage personalized information about human users to inform a robot's decision-making and task execution. I model users through two key aspects of cognitive state: their latent cognitive skills and inferred mental models. These models are applied to practical human-robot teaming problems. Additionally, I explore how robot error type impacts user responses and team performance in structured shared decision-making. The findings presented in this dissertation enable novel teaming capabilities that adapt to individuals' prior knowledge and real-time belief states.
Sponsor
Date
2025-04-28
Extent
Resource Type
Text
Resource Subtype
Dissertation