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	Learning basic unit movements for humanoid arm motion control
	Hu, Fan ; Wu, Xihong ; Luo, Dingsheng
	2016
英文摘要	Manipulation skill is important for humanoid robots to live and work with humans, and arm motion control is essential for the manipulation accomplishment. In our research, we hope our robot execute a manipulation task by combining basic unit movements (BUMs), thus making the manipulation easier and more robust. So in this paper, we firstly define BUMs which actually can be regarded as basic components of any arm motion. Then we propose a learning approach for the robot to execute BUMs, which means knowing the current state, the robot learns how to move his arm to accomplish the given BUM. Considering the complexity and inaccuracy problems in solving the inverse kinematics, the proposed approach is basically building an internal inverse model and the robot directly learns in the motor space without any inverse kinematics. Taking advantages of the powerful capacity of Deep Neural Networks (DNN) in extracting inherent features, the auto-encoder is employed to formalize our model. Experimental results on MATLAB simulation as well as PKU-HR5II humanoid robot reveal the effectiveness of the proposed approach. The robot can successfully execute almost all the BUMs in the whole workspace of his right arm with the accuracy of 98.49%. ? 2016 IEEE.; EI; 327-333
语种	英语
出处	14th IEEE International Workshop on Advanced Motion Control, AMC 2016
DOI标识	10.1109/AMC.2016.7496371
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449447]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hu, Fan,Wu, Xihong,Luo, Dingsheng. Learning basic unit movements for humanoid arm motion control. 2016-01-01.

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