contributor author | Hollinger, G.A. , Sukhatme, G.S. | |
date accessioned | 2020-03-12T20:49:17Z | |
date available | 2020-03-12T20:49:17Z | |
date issued | 2014 | |
identifier other | 6907833.pdf | |
identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1026699?show=full | |
format | general | |
language | English | |
publisher | IEEE | |
title | Trajectory learning for human-robot scientific data collection | |
type | Conference Paper | |
contenttype | Metadata Only | |
identifier padid | 8151762 | |
subject keywords | neural nets | |
subject keywords | rendering (computer graphics) | |
subject keywords | solid modelling | |
subject keywords | 3D content streaming and rendering system | |
subject keywords | artificial neural network based predictor | |
subject keywords | client machine | |
subject keywords | dynamic 3D object | |
subject keywords | learning process | |
subject keywords | neural network based predictors | |
subject keywords | static 3D object | |
subject keywords | Computational modeling | |
subject keywords | Heuristic algorithms | |
subject keywords | Prediction algorithms | |
subject keywords | Predictive models | |
subject keywords | Solid modeling | |
subject keywords | Three-dimensional displays | |
subject keywords | Training | |
subject keywords | 3D Streaming | |
subject keywords | Artificial Neural Network | |
subject keywords | Machine Learning | |
subject keywords | Predictive Model | |
subject keywords | Progressive Meshing | |
subject keywords | User Inte | |
identifier doi | 10.1109/ICSEC.2014.6978239 | |
journal title | obotics and Automation (ICRA), 2014 IEEE International Conference on | |
filesize | 679977 | |
citations | 0 | |