A Framework for Adapting Human-Robot Interaction to Diverse User Groups
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arXiv
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| Format: | Preprint |
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2024
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| author | Rosin, Theresa Pekarek Hassouna, Vanessa Sun, Xiaowen Krohm, Luca Kordt, Henri-Leon Beetz, Michael Wermter, Stefan |
| author_facet | Rosin, Theresa Pekarek Hassouna, Vanessa Sun, Xiaowen Krohm, Luca Kordt, Henri-Leon Beetz, Michael Wermter, Stefan |
| contents | To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on user feedback. While previous research often focuses on specific demographics, we present a novel framework for adaptive Human-Robot Interaction (HRI) that tailors interactions to different user groups and enables individual users to modulate interactions through both minor and major interruptions. Our primary contributions include the development of an adaptive, ROS-based HRI framework with an open-source code base. This framework supports natural interactions through advanced speech recognition and voice activity detection, and leverages a large language model (LLM) as a dialogue bridge. We validate the efficiency of our framework through module tests and system trials, demonstrating its high accuracy in age recognition and its robustness to repeated user inputs and plan changes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Framework for Adapting Human-Robot Interaction to Diverse User Groups Rosin, Theresa Pekarek Hassouna, Vanessa Sun, Xiaowen Krohm, Luca Kordt, Henri-Leon Beetz, Michael Wermter, Stefan Robotics Computation and Language Human-Computer Interaction To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on user feedback. While previous research often focuses on specific demographics, we present a novel framework for adaptive Human-Robot Interaction (HRI) that tailors interactions to different user groups and enables individual users to modulate interactions through both minor and major interruptions. Our primary contributions include the development of an adaptive, ROS-based HRI framework with an open-source code base. This framework supports natural interactions through advanced speech recognition and voice activity detection, and leverages a large language model (LLM) as a dialogue bridge. We validate the efficiency of our framework through module tests and system trials, demonstrating its high accuracy in age recognition and its robustness to repeated user inputs and plan changes. |
| title | A Framework for Adapting Human-Robot Interaction to Diverse User Groups |
| topic | Robotics Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.11377 |