Haptic Material Classification with a Multi-Channel Neural Network

We present a novel approach for haptic material classification based on an adaptation of human haptic exploratory procedures executed by a robot arm with an optical force sensor. A multi-channel neural architecture informed by findings from human haptic perception performs a spectral analysis on vibration and texture data gathered during material exploration and integrates this analysis with information gathered on material compliance. Experimental results show a high classification accuracy on a test set of 32 common household materials. Furthermore, we show that haptic material properties, relevant for robot grasping, can be classified with a simple haptic exploration while actual material classification requires more complex exploration and computation.

@InProceedings{KANW17, 
 	 author =  {Kerzel, Matthias and Ali, Moaaz Maamoon Mohammed and Ng, Hwei Geok and Wermter, Stefan},  
 	 title = {Haptic Material Classification with a Multi-Channel Neural Network}, 
 	 booktitle = {International Joint Conference on Neural Networks (IJCNN)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {439--446},
 	 year = {2017},
 	 month = {May},
 	 publisher = {IEEE},
 	 doi = {10.1109/IJCNN.2017.7965887}, 
 }