When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration
<|reference_start|>When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration: 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.<|reference_end|>
@article{allgeuer2024when,
    title={When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation
  and Collaboration},
    author={Philipp Allgeuer, Hassan Ali and Stefan Wermter},
    journal={International Conference on Artificial Neural Networks, Sep 2024
  (pp. 306-321)},
    year={2024},
    doi={10.1007/978-3-031-72341-4_21},
    archivePrefix={arXiv},
    eprint={2407.00518},
    primaryClass={cs.RO}
}