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用于机器人智能的深度因果学习。

Deep causal learning for robotic intelligence.

作者信息

Li Yangming

机构信息

RoCAL, Rochester Institute of Technology, Rochester, NY, United States.

出版信息

Front Neurorobot. 2023 Feb 22;17:1128591. doi: 10.3389/fnbot.2023.1128591. eCollection 2023.

DOI:10.3389/fnbot.2023.1128591
PMID:36910267
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9992986/
Abstract

This invited Review discusses causal learning in the context of robotic intelligence. The Review introduces the psychological findings on causal learning in human cognition, as well as the traditional statistical solutions for causal discovery and causal inference. Additionally, we examine recent deep causal learning algorithms, with a focus on their architectures and the benefits of using deep nets, and discuss the gap between deep causal learning and the needs of robotic intelligence.

摘要

这篇特邀综述讨论了机器人智能背景下的因果学习。该综述介绍了人类认知中因果学习的心理学研究结果，以及因果发现和因果推断的传统统计解决方案。此外，我们研究了最近的深度因果学习算法，重点关注其架构以及使用深度网络的优势，并讨论了深度因果学习与机器人智能需求之间的差距。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/5b35ca24284f/fnbot-17-1128591-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/73dc30becf82/fnbot-17-1128591-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/3704df74bec1/fnbot-17-1128591-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/cf7ef3c86880/fnbot-17-1128591-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/9b9da8baee0c/fnbot-17-1128591-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/5b35ca24284f/fnbot-17-1128591-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/73dc30becf82/fnbot-17-1128591-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/bf6d61e5c75c/fnbot-17-1128591-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/3704df74bec1/fnbot-17-1128591-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9445/9992986/cf7ef3c86880/fnbot-17-1128591-g0004.jpg
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