Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration

Author:

Liu Haokun1ORCID,Zhu Yaonan2ORCID,Kato Kenji3ORCID,Tsukahara Atsushi3ORCID,Kondo Izumi3ORCID,Aoyama Tadayoshi4ORCID,Hasegawa Yasuhisa4ORCID

Affiliation:

1. Department of Mechanical Systems Engineering, Nagoya University, Nagoya, Japan

2. School of Engineering, The University of Tokyo, Tokyo, Japan

3. National Center for Geriatrics and Gerontology, Obu, Japan

4. Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan

Funder

JST SICORP, Japan

JSPS KAKENHI

NCGG through Chojuiryou Kenkyukaihatsuhi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference21 articles.

1. LLaMA: Open and efficient foundation language models;Touvron,2023

2. Do as I can and not as I say: Grounding language in robotic affordances;Ahn,2022

3. TidyBot: personalized robot assistance with large language models

4. You Only Look Once: Unified, Real-Time Object Detection

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