Learning representations for robust human–robot interaction

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2024-10-21 DOI:10.1002/aaai.12197
Yen-Ling Kuo
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Abstract

This article summarizes the author's presentation in the New Faculty Highlight at the Thirty-Eighth AAAI Conference on Artificial Intelligence. It discusses the desired properties of representations for enabling robust human–robot interaction. Examples from the author's work are presented to show how to build these properties into models for performing tasks with natural language guidance and engaging in social interactions with other agents.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
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Issue Information Leveraging AI to improve health information access in the World's largest maternal mobile health program Introduction to the special issue on Innovative Applications of Artificial Intelligence (IAAI 2024) Learning representations for robust human–robot interaction AI-assisted research collaboration with open data for fair and effective response to call for proposals
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