Understanding Human-Autonomy Teams Through a Human-Animal Teaming Model

Area of Science:

• Human-Robot Interaction (HRI)
• Artificial Intelligence (AI)
• Cognitive Science
• Robotics

Background:

• Human social competencies are complex, involving interpretation, prediction, and reaction to others' behavior.
• Developing computational systems with similar social abilities is crucial for intelligent human-computer agents.
• Human-animal teams provide a potential analog for understanding and building effective human-autonomy teams.

Purpose of the Study:

• To explore the utility of human-animal team analogs for creating accurate mental models of robots.
• To identify challenges and limitations in using human-animal team models for human-autonomy teams.
• To review existing research on human-animal interactions and their computational representations.

Main Methods:

• Literature review of human-animal team research and computational modeling.
• Analysis of existing digital twin models of human-animal teams.
• Identification of key issues and challenges in applying the human-animal analogy to human-autonomy systems.

Main Results:

• Current digital twins of human-animal teams are often incomplete, limiting their direct application.
• The human-animal analogy offers a framework for fostering veridical mental models of robots.
• Significant challenges exist in translating insights from human-animal dynamics to human-robot collaboration.

Conclusions:

• Human-animal team analogs are valuable for advancing robot social intelligence and human-robot interaction.
• Further research is needed to refine computational models and address limitations in the analogy.
• Accurate mental models are essential for robots to function as capable partners in human-autonomy teams.