Human-Robot Teaming for a Cooperative Game in a Shared Partially Observable Space
Brendan Mooers, Audrey L. Aldridge, Andrew Buck, Cindy L. Bethel, Derek T. Anderson
Proc. SPIE 12525, Geospatial Informatics XIII, 2023
SPIE, Human-Robot Interaction, Pathfinding, MCDM, Mental Map
Abstract
An open research question is how to best pair a human and agent (e.g., AI, autonomous) relative to a complex, multi-objective task in a dynamic and unknown partially observable environment. At the heart of this challenge resides even deeper questions like what AI is needed and how can bi-directional and multi-directional human-robot trust be established. In this paper, the theoretical framework for a simple 2D grid world-based cooperative search and rescue game is explored. The resultant prototype interface enables the study of human-robot interaction for human-robot teaming. First, the design and implementation of a prototype interface is discussed. A 2D gridworld was selected to simplify the investigation and eliminate confounding factors that arise in more complicated simulated 3D and real world experiments. Next, different types of autonomous agents are introduced, as they impact our studies and ultimately are an integral element of the underlying research question. This is followed by three levels of increasing complexity open-ended games, easy, medium, and hard. The current paper does not contain human experimentation results. That is the next step in this research. Instead, this article introduces, explains, and defends a set of design choices and working examples are provided to facilitate open discussion.
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