Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation
In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to f...
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Format: | Article |
Language: | English |
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Frontiers Media S.A.
2024-04-01
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Series: | Frontiers in Robotics and AI |
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Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2024.1347538/full |
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author | Younes Lakhnati Max Pascher Max Pascher Jens Gerken |
author_facet | Younes Lakhnati Max Pascher Max Pascher Jens Gerken |
author_sort | Younes Lakhnati |
collection | DOAJ |
description | In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems. |
first_indexed | 2024-04-24T14:25:58Z |
format | Article |
id | doaj.art-a7f59dbeddb5486d98cd0cda386e3c63 |
institution | Directory Open Access Journal |
issn | 2296-9144 |
language | English |
last_indexed | 2024-04-24T14:25:58Z |
publishDate | 2024-04-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Robotics and AI |
spelling | doaj.art-a7f59dbeddb5486d98cd0cda386e3c632024-04-03T05:10:39ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442024-04-011110.3389/frobt.2024.13475381347538Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulationYounes Lakhnati0Max Pascher1Max Pascher2Jens Gerken3Inclusive Human-Robot-Interaction, TU Dortmund University, Dortmund, NW, GermanyInclusive Human-Robot-Interaction, TU Dortmund University, Dortmund, NW, GermanyHuman-Computer Interaction, University of Duisburg-Essen, Essen, NW, GermanyInclusive Human-Robot-Interaction, TU Dortmund University, Dortmund, NW, GermanyIn a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel simulation framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with simulated robot agents through natural language, each powered by individual GPT cores. By means of OpenAI’s function calling, we bridge the gap between unstructured natural language input and structured robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a simulated multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their simulated robot collaborators. Still, those users who did explore were able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.https://www.frontiersin.org/articles/10.3389/frobt.2024.1347538/fullassistive robotsvirtual realityevaluationshared controlvariable autonomylarge language model |
spellingShingle | Younes Lakhnati Max Pascher Max Pascher Jens Gerken Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation Frontiers in Robotics and AI assistive robots virtual reality evaluation shared control variable autonomy large language model |
title | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation |
title_full | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation |
title_fullStr | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation |
title_full_unstemmed | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation |
title_short | Exploring a GPT-based large language model for variable autonomy in a VR-based human-robot teaming simulation |
title_sort | exploring a gpt based large language model for variable autonomy in a vr based human robot teaming simulation |
topic | assistive robots virtual reality evaluation shared control variable autonomy large language model |
url | https://www.frontiersin.org/articles/10.3389/frobt.2024.1347538/full |
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