Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction

doi:10.1007/s12559-020-09753-1

	Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction
	Zhang, Tielin 1; Zeng, Yi 1,2,3,4; Pan, Ruihan 5; Shi, Mengting 1,4; Lu, Enmeng 1
刊名	COGNITIVE COMPUTATION
	2020-07-14
页码	13
关键词	Brain-inspired architecture Procedural knowledge Deep neural network Reinforcement learning Knowledge graph
ISSN号	1866-9956
DOI	10.1007/s12559-020-09753-1
通讯作者	Zhang, Tielin(tielin.zhang@ia.ac.cn) ; Zeng, Yi(yi.zeng@ia.ac.cn)
英文摘要	Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative knowledge in the cognitive system. Inspired by the architecture of the human brain which has integrated well different kinds of cognitive functions, a Brain-inspired Active Learning Architecture (BALA) is proposed for procedural knowledge understanding based on Baxter robot and human interaction. The BALA model contains four main parts: inspired by Primary Visual Pathway, a Convolutional Neural Network (CNN) is constructed for spatial information abstraction; inspired by the Hippocampus Pathway (especially the recurrent loops in CA3 sub-region), a Recurrent Neural Network (RNN) is built for sequential information processing related with procedural knowledge; inspired by the Prefrontal Cortex, a Knowledge Graph based on Bag Of Words (BOW) is constructed for declarative knowledge generation and association; inspired by the Basal Ganglia Pathway, we select Q matrix for Reinforcement Learning (RL). The CNN and RNN parts will be firstly pre-trained on ImageNet dataset and standard Youtube Video-Scene dataset respectively. Then, the RNN, Knowledge Graph, and Q matrix will be dynamically updated in the Baxter robot's interactive learning procedure with human cooperators. The BALA could actively and incrementally recognize different kinds of procedural knowledge. In 22-type daily-life videos with procedure knowledge (e.g., opening the door, wiping the table, or taking the phone), the BALA model gets the best performance compared with standard CNN, RNN, RL, and other integrative methods. The BALA model is a small step on integrative intelligence interaction between the Baxter robot and human cooperator.
资助项目	National Natural Science Foundation of China[61806195] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32070100] ; Beijing Municipality of Science and Technology[Z181100001518006] ; Major Research Program of Shandong Province[2018CXGC1503] ; CETC Joint Fund[6141B08010103]
WOS研究方向	Computer Science ; Neurosciences & Neurology
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000548792300001
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/11350]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Zhang, Tielin; Zeng, Yi
作者单位	1.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Res Ctr Intelligent Secur Technol, Chongqing Inst Green & Intelligent Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Tielin,Zeng, Yi,Pan, Ruihan,et al. Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction[J]. COGNITIVE COMPUTATION,2020:13.
APA	Zhang, Tielin,Zeng, Yi,Pan, Ruihan,Shi, Mengting,&Lu, Enmeng.(2020).Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction.COGNITIVE COMPUTATION,13.
MLA	Zhang, Tielin,et al."Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction".COGNITIVE COMPUTATION (2020):13.