Learning to Identify Container Contents Through Tactile Vibration Signatures

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

IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2016.



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.