Active Prior Tactile Knowledge Transfer for Learning Tactual Properties of New Objects

Sensors
Di FengGordon Cheng

Abstract

Reusing the tactile knowledge of some previously-explored objects (prior objects) helps us to easily recognize the tactual properties of new objects. In this paper, we enable a robotic arm equipped with multi-modal artificial skin, like humans, to actively transfer the prior tactile exploratory action experiences when it learns the detailed physical properties of new objects. These experiences, or prior tactile knowledge, are built by the feature observations that the robot perceives from multiple sensory modalities, when it applies the pressing, sliding, and static contact movements on objects with different action parameters. We call our method Active Prior Tactile Knowledge Transfer (APTKT), and systematically evaluated its performance by several experiments. Results show that the robot improved the discrimination accuracy by around 10 % when it used only one training sample with the feature observations of prior objects. By further incorporating the predictions from the observation models of prior objects as auxiliary features, our method improved the discrimination accuracy by over 20 % . The results also show that the proposed method is robust against transferring irrelevant prior tactile knowledge (negative knowledge tra...Continue Reading

References

Oct 1, 1990·Cognitive Psychology·S J Lederman, R L Klatzky
Jul 1, 1987·Cognitive Psychology·S J Lederman, R L Klatzky
Jul 12, 2012·Frontiers in Neurorobotics·Jeremy A Fishel, Gerald E Loeb

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Citations

Dec 11, 2019·Sensors·Francisco PastorJesús M Gómez-de-Gabriel
Nov 23, 2019·Sensors·José C V S JuniorMarcelo A C Fernandes
Aug 23, 2020·Sensors·Tibor Barši PalmićMiha Boltežar
Jan 28, 2021·Frontiers in Robotics and AI·Francesca PalermoIldar Farkhatdinov

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Methods Mentioned

BETA
feature extraction

Software Mentioned

APTKT

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