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arXiv · 1902.09241

Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Huanbo Sun·Goerg Martius
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Abstract

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 and obtain 8 mm localization precision.

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Huanbo Sun, Goerg Martius. 2019-02-25. Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns. https://arxiv.org/abs/1902.09241

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