Learning to identify container contents through tactile vibration signatures

Carolyn L. Chen, Jeffrey O. Snyder, Peter Jeffrey Ramadge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

We examine using a simple contact sensor coupled with standard machine learning algorithms to classify and count objects shaken in a container. The contact sensor measures the resulting vibrations, and these signatures are used to learn a classifier that maps vibration signatures to known object categories. A linear support vector machine trained on labeled vibration signatures achieves a mean binary classification accuracy of 99% over 66 pairs of objects and a mean multi-class classification accuracy of 94% over 12 classes. It is also shown that useful tasks such as approximate counting of objects over the range 1 to 10 is possible. We see potential applications of these ideas in service robots engaged in cleanup and inventory control in labs, workshops, stores, warehouses and homes.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-48
Number of pages6
ISBN (Electronic)9781509046164
DOIs
StatePublished - Feb 22 2017
Event2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016 - San Francisco, United States
Duration: Dec 13 2016Dec 16 2016

Publication series

Name2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016

Other

Other2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots, SIMPAR 2016
CountryUnited States
CitySan Francisco
Period12/13/1612/16/16

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Artificial Intelligence

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