IEEE：2025具身智能（Embodied AI）综述：从模拟器到研究任务的调查分析报告（英文版）（15页）.pdf

1、This article has been accepted for inclusion in a future issue of this journal.Content is final as presented,with the exception of pagination.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE1A Survey of Embodied AI:From Simulatorsto Research TasksJiafei Duan,Samson Yu,Hui Li Tan,Ho

2、ngyuan Zhu,and Cheston TanAbstractThere has been an emerging paradigm shift from theera of“internet AI”to“embodied AI,”where AI algorithms andagents no longer learn from datasets of images,videos or textcurated primarily from the internet.Instead,they learn throughinteractionswiththeirenvironmentsfr

3、omanegocentricperceptionsimilartohumans.Consequently,therehasbeensubstantialgrowthin the demand for embodied AI simulators to support variousembodied AI research tasks.This growing interest in embodied AIis beneficial to the greater pursuit of Artificial General Intelligence(AGI),but there has not b

4、een a contemporary and comprehensivesurvey of this field.This paper aims to provide an encyclopedicsurvey for the field of embodied AI,from its simulators to itsresearch.By evaluating nine current embodied AI simulators withour proposed seven features,this paper aims to understand thesimulators in t

5、heir provision for use in embodied AI research andtheirlimitations.Lastly,thispapersurveysthethreemainresearchtasks in embodied AI visual exploration,visual navigation andembodied question answering(QA),covering the state-of-the-artapproaches,evaluation metrics and datasets.Finally,with the newinsig

6、htsrevealedthroughsurveyingthefield,thepaperwillprovidesuggestionsforsimulator-for-taskselectionsandrecommendationsfor the future directions of the field.Index TermsEmbodied AI,computer vision,3D simulators.I.INTRODUCTIONRECENT advances in deep learning,reinforcement learn-ing,computer graphics and