| Autor: |
Allgeuer, Philipp, Ali, Hassan, Wermter, Stefan |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
International Conference on Artificial Neural Networks, Sep 2024 (pp. 306-321) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/978-3-031-72341-4_21 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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