Chat with the Environment: Interactive Multimodal Perception Using Large Language Models-Reference-Cited by-同舟云学术

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Chat with the Environment: Interactive Multimodal Perception Using Large Language Models

Published:2023-10-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
language:
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Author:

Zhao Xufeng¹,Li Mengdi¹,Weber Cornelius¹,Hafez Muhammad Burhan¹,Wermter Stefan¹

Affiliation:

1. Universität Hamburg,Knowledge Technology Group,Department of Informatics,Hamburg,Germany,22527

Funder

German Research Foundation (DFG) in the project Crossmodal Learning

China Scholarship Council (CSC)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10341341/10341342/10342363.pdf?arnumber=10342363

Reference30 articles.

1. Do as i can, not as i say: Grounding language in robotic affordances;Ahn;arXiv preprint,2022

2. Multimodal deep learning;Akkus;arXiv preprint,2023

3. Active Information Acquisition with Mobile Robots;Atanasov;University of Pennsylvania,2015

4. Cross Modal Disambiguation

5. RT-2: Vision-language-action models transfer web knowledge to robotic control;Brohan;arXiv Preprint,2023

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2. Unlocking Robotic Autonomy: A Survey on the Applications of Foundation Models;International Journal of Control, Automation and Systems;2024-08

3. The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare;Robotics;2024-07-23

4. A Review of Key Technologies for Emotion Analysis Using Multimodal Information;Cognitive Computation;2024-06-01

5. Enabling action crossmodality for a pretrained large language model;Natural Language Processing Journal;2024-06

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