BAS Optimized ELM for KUKA iiwa Robot Learning

Chunxu Li*, Shuo Zhu, Zhongbo Sun, James Rogers

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In this brief, an enhanced robotic learning interface has been investigated using Beetle Antennae Search (BAS) and Extreme Learning Machine (ELM). The initial values of learning weights and bias of the network have significant effect on the performance of the ELM, hence, BAS algorithm was employed to optimize the initial values of learning weights and bias. Kinect v2 camera sensor was applied to obtain the endpoint's position of the upper limb, MYO armband was used to measure the corresponding joint angle values. Those aforementioned data formed the dataset to be trained by ELM and after training the ELM model was able to generate angle values by only giving position as input without a need to carry out kinematic calculations. The proposed method has been validated by conducting series of experimental studies on a KUKA iiwa robot.
Original languageEnglish
Pages (from-to)1-1
Number of pages0
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume0
Issue number0
Early online date29 Oct 2020
DOIs
Publication statusPublished - 29 Oct 2020

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