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Paper TuA06.3

Jamali, Nawid (University of New South Wales), Sammut, Claude (The University of New South Wales)

Slip Prediction Using Hidden Markov Models: Multidimensional Sensor Data to Symbolic Temporal Pattern Learning

Scheduled for presentation during the Regular Session "Applied Machine Learning" (TuA06), Tuesday, May 15, 2012, 09:00−09:15, Meeting Room 6 (Oya'te)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Force and Tactile Sensing, Dexterous Manipulation, Grasping

Abstract

We present experiments on the application of machine learning to predicting slip. The sensing information is provided by a force/torque sensor and an artificial finger, which has randomly distributed strain gauges and polyvinylidene fluoride (PVDF) films embedded in silicone resulting in multidimensional time series data on the finger-object contact. An incipient slip is detected by studying temporal patterns in the data. The data is analysed using probabilistic clustering that transforms the data into a sequence of symbols, which is used to train a hidden Markov model (HMM) classifier. Experimental results show that the classifier can predict a slip, at least 100ms before a slip takes place, with an accuracy of 96% on the validation set.

 

 

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