Linear dynamic system method for tactile object classification  

作　　者：MA Rui LIU HuaPing SUN FuChun YANG QingFen GAO Meng 

机构地区：[1]Department of Electrical and Electronic Engineering, Shijiazhuang Tiedao University [2]Department of Computer Science and Technology, Tsinghua University [3]Tsinghua National Laboratory for Information Science and Technology

出　　处：《Science China(Information Sciences)》2014年第12期43-53,共11页中国科学（信息科学）（英文版）

基　　金：supported by National Key Project for Basic Research of China(Grant No.2013CB329403);National Natural Science Foundation of Major International Cooperation Research Projects(Grant No.61210013);Tsinghua Self-innovation Project(Grant No.20111081111);Tsinghua University Initiative Scientific Research Program(Grant No.20131089295)

摘　　要：Lots of tactile sequences can be obtained by using a dexterous hand for grasping different objects. The ability of robotic environmental perception and dexterous manipulation will be significantly improved after these tactile sequences are correctly classified. Therefore, tactile sequences are separated into series of subgroups, and a method based on linear dynamical system （LDS） is used to extract features. Since these LDSs lie in non- Euclidean space, the Martin distance, which is a measurement different from Euclidean distance, is applied to calculate the distance between two LDSs, and the K-Medoid algorithm is used for clustering. The codebook is obtained after clustering and is used to represent time sequences to get a Bag-of-System （BoS）. Then the BoS and labels are sent to Extreme Learning Machine （ELM） to train a classifier. Finally, three databases, KTH-7, KTH-10 and TSH-8 are used to evaluate our algorithm.Lots of tactile sequences can be obtained by using a dexterous hand for grasping different objects. The ability of robotic environmental perception and dexterous manipulation will be significantly improved after these tactile sequences are correctly classified. Therefore, tactile sequences are separated into series of subgroups, and a method based on linear dynamical system （LDS） is used to extract features. Since these LDSs lie in non- Euclidean space, the Martin distance, which is a measurement different from Euclidean distance, is applied to calculate the distance between two LDSs, and the K-Medoid algorithm is used for clustering. The codebook is obtained after clustering and is used to represent time sequences to get a Bag-of-System （BoS）. Then the BoS and labels are sent to Extreme Learning Machine （ELM） to train a classifier. Finally, three databases, KTH-7, KTH-10 and TSH-8 are used to evaluate our algorithm.

关 键 词：tactile sequence linear dynamic system bag-of-system extreme learning machine CLASSIFICATION 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TQ172.11[自动化与计算机技术—控制科学与工程]