Tag: Karen Feigh

Yosef Razin’s article on HRI trust accepted at the ACM Transactions on Human-Robot Interactions

Abstract: Trust is crucial for technological acceptance, continued usage, and teamwork. However, human-robot trust, and human-machine trust more generally, suffer from terminological disagreement and construct proliferation. By comparing, mapping, and analyzing well-constructed trust survey instruments, this work uncovers a consensus structure of trust in human-machine interaction. To do so, we identify the most frequently cited […]

Simulated Mental Models and Active Replanning for Human-Robot Interaction

This project Introduces a communication framework to facilitate efficient information synchronization between an autonomous system and a human operator under scenarios where instant data transfer is not available. We utilize mental models to represent the system’s high-level state and employ a replanning algorithm to adapt to the dynamically changing environment in real time.  Our work […]

Human-AI Interaction in Autonomous Aerial Vehicles: A MedEvac Scenario

This project explores the interaction between human operators (novice flight medics) and AI pilots in autonomous aerial vehicles during medical evacuation situations. The primary objective is to evaluate how changes in workload and cognitive biases influence the fluency of human-AI interaction and overall mission effectiveness. Through simulated medical evaluation scenarios, this research seeks to assess […]

STARLIT – xGEO Wargames

This project aims to design wargames for space scenarios for the next 20 years, particularly in the xGEO domain. The xGEO domain presents new challenges as many operators do not have experience in this domain, resulting in training gaps. We are focused on designing scenarios specific to the cislunar space. Starting now, this project is […]

Exploring Shared Mental Models in Household Human-Robot Teams

In human-human teams, we often infer the situation awareness of our teammates to inform our planning and decision-making. What if we applied this to human-AI teams? This project explores how autonomous systems can estimate the belief states of human teammates. We deploy a robot agent to a collaborative household cooking domain, where the agent constructs […]

Project SURI: Multi-Phenomenological, Autonomous, and Understandable SDA and XDA Decision Support

As a multi-institutional effort between CU Boulder, Georgia Tech, and Texas A&M, this research serves to utilize developments in cognitive engineering, autonomy, and decision-making in the context of modern astrodynamics to improve multi-target tracking and dim object detection. While CU Boulder & Texas A&M will be focusing on sensor exploitation, placement, and processing, the CEC […]

Prof. Feigh Participates in NASA’s Imagin Aviation Workshop

Prof. Karen Feigh participated in the Imagin Aviation Workshop Panel that discussed Advanced Air Mobility: Moving Through the Tech and Innovation Lifecycle. The outlook for aviation is looking decidedly futuristic with capabilities like eVTOL air taxis, autonomous regional transport, and drone delivery already on the horizon. Advanced air mobility (AAM) has captured the public’s imagination […]

Walsh presents at IEEE International Conference on Systems, Man, and Cybernetics

Sarah Walsh presented her paper ‘Mental Models of AI Performance and Bias of Nontechnical Users’ at the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). This work investigates users’ mental models of an AI-decision aid. The paper details an experiment designed within the ‘Disaster Relief’ experimental environment. 

Ranjani Narayanan presents at 67th Annual Meeting of the Human Factors and Ergonomics Society

Ranjani presented her paper along with Sarah Walsh on “Development of Mental Models in Decision Making Tasks” at the 67th Annual Meeting of the Human Factors and Ergonomics Society (HFES) in Washington D.C. This work investigates the dynamic behavior of users’ decision strategies and the stability and predictability of their mental models over time.