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迈向认知机器人学的地道框架。

Toward an idiomatic framework for cognitive robotics.

作者信息

Damgaard Malte Rørmose, Pedersen Rasmus, Bak Thomas

机构信息

Department of Electronic Systems, Automation and Control, Aalborg University, 9220 Aalborg, Denmark.

出版信息

Patterns (N Y). 2022 Jul 8;3(7):100533. doi: 10.1016/j.patter.2022.100533.

DOI:10.1016/j.patter.2022.100533
PMID:35845837
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9278519/
Abstract

Inspired by the "cognitive hourglass" model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications.

摘要

受名为Sigma的认知架构背后的研究人员提出的“认知沙漏”模型启发,我们提出了一个用于开发认知机器人认知架构的框架。所提出框架的主要目的是通过鼓励现有成果的合作与重用,简化认知架构的开发。这是通过提出一个将认知架构开发划分为一系列可部分独立考虑的层次的框架来实现的,其中一些层次直接与其他研究领域相关。最后,我们介绍并回顾了所提出框架必不可少的一些主题。我们还概述了一组应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/28ac85c206b6/gr14.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/b7fd702bcc8b/gr9.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/8ca1793d5855/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/b62f14844718/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/e97b0fc4175f/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/28ac85c206b6/gr14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/0de2fdecb652/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/52473b4a1a74/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/6e7a0daf3ea2/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/86a9277e65d0/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/2aeca76b2c7f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/39f87b4c6c20/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/12839126037f/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/731b3740e288/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/273c0951297b/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/b7fd702bcc8b/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/c9715079d555/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/8ca1793d5855/gr11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/b62f14844718/gr12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/e97b0fc4175f/gr13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/01ca/9278519/28ac85c206b6/gr14.jpg

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本文引用的文献

1
Stan: A Probabilistic Programming Language.斯坦:一种概率编程语言。
J Stat Softw. 2017;76. doi: 10.18637/jss.v076.i01. Epub 2017 Jan 11.
2
A Socially Adaptable Framework for Human-Robot Interaction.一种用于人机交互的社会适应性框架。
Front Robot AI. 2020 Oct 19;7:121. doi: 10.3389/frobt.2020.00121. eCollection 2020.
3
Integrated Cognitive Architecture for Robot Learning of Action and Language.用于机器人动作与语言学习的集成认知架构
Front Robot AI. 2019 Nov 29;6:131. doi: 10.3389/frobt.2019.00131. eCollection 2019.
4
Array programming with NumPy.使用 NumPy 进行数组编程。
Nature. 2020 Sep;585(7825):357-362. doi: 10.1038/s41586-020-2649-2. Epub 2020 Sep 16.
5
Advances in Variational Inference.变分推理的进展
IEEE Trans Pattern Anal Mach Intell. 2019 Aug;41(8):2008-2026. doi: 10.1109/TPAMI.2018.2889774. Epub 2018 Dec 25.
6
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model.SERKET:一种连接随机模型以实现大规模认知模型的架构。
Front Neurorobot. 2018 Jun 26;12:25. doi: 10.3389/fnbot.2018.00025. eCollection 2018.
7
A computational model of perception and action for cognitive robotics.一种用于认知机器人技术的感知与行动计算模型。
Cogn Process. 2011 Nov;12(4):355-65. doi: 10.1007/s10339-011-0408-x. Epub 2011 May 20.
8
Rational choice and the structure of the environment.理性选择与环境结构
Psychol Rev. 1956 Mar;63(2):129-38. doi: 10.1037/h0042769.