When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917788620161024 |
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| author | Allgeuer, Philipp Ali, Hassan Wermter, Stefan |
| author_facet | Allgeuer, Philipp Ali, Hassan Wermter, Stefan |
| contents | We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and extensible methodology for grounding an LLM with the sensory perceptions and capabilities of a physical robot, and integrate multiple deep learning models throughout the architecture in a form of system integration. The integrated models encompass various functions such as speech recognition, speech generation, open-vocabulary object detection, human pose estimation, and gesture detection, with the LLM serving as the central text-based coordinating unit. The qualitative and quantitative results demonstrate the huge potential of LLMs in providing emergent cognition and interactive language-oriented control of robots in a natural and social manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00518 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration Allgeuer, Philipp Ali, Hassan Wermter, Stefan Robotics We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and extensible methodology for grounding an LLM with the sensory perceptions and capabilities of a physical robot, and integrate multiple deep learning models throughout the architecture in a form of system integration. The integrated models encompass various functions such as speech recognition, speech generation, open-vocabulary object detection, human pose estimation, and gesture detection, with the LLM serving as the central text-based coordinating unit. The qualitative and quantitative results demonstrate the huge potential of LLMs in providing emergent cognition and interactive language-oriented control of robots in a natural and social manner. |
| title | When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.00518 |