Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment
Kimberlee Chestnut Chang, Reed Jensen, Rohan Paleja, Sam L. Polk, Rob, Seater, Jackson Steilberg, Curran Schiefelbein, Melissa Scheldrup, Matthew, Gombolay, and Mabel D. Ramirez

TL;DR
This paper proposes a novel asynchronous training paradigm where humans learn to coordinate with autonomous teammates in a partially observable environment, reducing scheduling constraints and enabling scalable cooperative training.
Contribution
It introduces a new experimental design and clustering method for evaluating autonomous teammates in cooperative training with humans.
Findings
Autonomous teammates can effectively serve as training partners.
Clustering teammate trajectories simplifies experimental design.
The approach enables complex human-subjects studies in reasonable time.
Abstract
In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To reduce the often prohibitive scheduling constraints associated with cooperative training, this article introduces a paradigm for cooperative asynchronous training of human teams in which trainees practice coordination with autonomous teammates rather than humans. We introduce a novel experimental design for evaluating autonomous teammates for use as training partners in cooperative training. We apply the design to a human-subjects experiment where humans are trained with either another human or an autonomous teammate and are evaluated with a new human subject in a new, partially observable, cooperative game developed for this study. Importantly, we employ a method to cluster teammate trajectories from…
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Taxonomy
TopicsRobot Manipulation and Learning
