*In this paper, a neural network based transfer learning approach of inverse displacement analysis of robot manipulators is studied. Neural networks with different structures are applied utilizing data from different configurations of a manipulator for training process. The training is conducted on both the neural networks with pretrained initial parameters and the neural networks with random initialization. To investigate the rate of convergence of data fitting comprehensively, different values of performance target are defined. The computing epochs and performance measures are compared. It is presented that, depending on the structure of neural network, the proposed transfer learning may accelerate the training process and achieve higher accuracy. For different datasets, the transfer learning approach improves their performance differently.*


Neural Network Based Transfer Learning of Manipulator Inverse Displacement Analysis

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

IDETC2020 - Virtual Conference

Supporting the need among researchers worldwide for additional online and multimedia content, ASME now offers the ASME Conference Video Collection. This platform will collect and provide long-term access to conference presentation videos, encompassing all technology areas within ASME's technical conference and events program. About 30,000-40,000 engineers across all mechanical engineering and related disciplines participate in ASME conferences each year. This includes authors of approximately 6,000 to 8,000 technical papers published by ASME, the majority of whom also submit video presentations for our virtual conferences. The video collection will be available as an add-on to existing product packages or as a separate subscription.