Haptic Material Classification with a Multi-Channel Neural Network

Matthias Kerzel, Moaaz Maamoon Mohammed Ali, Hwei Geok Ng, Stefan Wermter
International Joint Conference on Neural Networks (IJCNN), pages 439--446 - May 2017.
Associated documents : Kerzel-Haptic_Material_Classification_2017-Webpage.pdf [5Mo]  
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.


  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)",
  pages        = "439--446",
  month        = "May",
  year         = "2017",
  publisher    = "IEEE",
  address      = "Anchorage, Alaska",
  key          = "Kerzel2017Haptic",
  url          = "https://www2.informatik.uni-hamburg.de/wtm/publications/2017/KANW17/"

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