Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

2018

Conference Paper

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Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

@conference{SunMartius2018:SingleTouchSensation,
  title = {Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns},
  author = {Sun, Huanbo and Martius, Georg},
  booktitle = {Proceedings International Conference on Humanoid Robots},
  pages = {846-853},
  publisher = {IEEE},
  address = {New York, NY, USA},
  year = {2018},
  note = {Oral Presentation},
  doi = {10.1109/HUMANOIDS.2018.8625064}
}