Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909439932497920 |
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| author | Chang, Kimberlee Chestnut Jensen, Reed Paleja, Rohan Polk, Sam L. Seater, Rob Steilberg, Jackson Schiefelbein, Curran Scheldrup, Melissa Gombolay, Matthew Ramirez, Mabel D. |
| author_facet | Chang, Kimberlee Chestnut Jensen, Reed Paleja, Rohan Polk, Sam L. Seater, Rob Steilberg, Jackson Schiefelbein, Curran Scheldrup, Melissa Gombolay, Matthew Ramirez, Mabel D. |
| contents | 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 demonstrations performed in the experiment to form a smaller number of training conditions. This results in a simpler experiment design that enabled us to conduct a complex cooperative training human-subjects study in a reasonable amount of time. Through a demonstration of the proposed experimental design, we provide takeaways and design recommendations for future research in the development of cooperative asynchronous training systems utilizing robot surrogates for human teammates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment Chang, Kimberlee Chestnut Jensen, Reed Paleja, Rohan Polk, Sam L. Seater, Rob Steilberg, Jackson Schiefelbein, Curran Scheldrup, Melissa Gombolay, Matthew Ramirez, Mabel D. Human-Computer Interaction Multiagent Systems Robotics 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 demonstrations performed in the experiment to form a smaller number of training conditions. This results in a simpler experiment design that enabled us to conduct a complex cooperative training human-subjects study in a reasonable amount of time. Through a demonstration of the proposed experimental design, we provide takeaways and design recommendations for future research in the development of cooperative asynchronous training systems utilizing robot surrogates for human teammates. |
| title | Asynchronous Training of Mixed-Role Human Actors in a Partially-Observable Environment |
| topic | Human-Computer Interaction Multiagent Systems Robotics |
| url | https://arxiv.org/abs/2412.17954 |