Learning Spatio Temporal Tactile Features with a ConvLSTM for the Direction Of Slip Detection

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ID: 113584
2019
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Abstract
Robotic manipulators have to constantly deal with the complex task of detecting whether a grasp is stable or, in contrast, whether the grasped object is slipping. Recognising the type of slippage—translational, rotational—and its direction is more challenging than detecting only stability, but is simultaneously of greater use as regards correcting the aforementioned grasping issues. In this work, we propose a learning methodology for detecting the direction of a slip (seven categories) using spatio-temporal tactile features learnt from one tactile sensor. Tactile readings are, therefore, pre-processed and fed to a ConvLSTM that learns to detect these directions with just 50 ms of data. We have extensively evaluated the performance of the system and have achieved relatively high results at the detection of the direction of slip on unseen objects with familiar properties (82.56% accuracy).
Reference Key
zapata-impata2019sensorslearning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Brayan S. Zapata-Impata;Pablo Gil;Fernando Torres;Zapata-Impata, Brayan S.;Gil, Pablo;Torres, Fernando;
Journal sensors
Year 2019
DOI
10.3390/s19030523
URL
https://www.mdpi.com/1424-8220/19/3/523
Keywords
Deep learning tactile processing direction of slip spatio-temporal feature learning

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