Dextrous manipulation requires effective tactile sensing. Our group has developed machine learning methods for an artificial finger to acquire the ability to distinguish textures and to recognise the precursors to slippage. This enables a manipulator to hold an object lightly, without slipping. The inspiration for the finger came through RoboCup contacts with the Asada laboratory in Osaka.  We reproduced the hardware and developed new machine learning methods for recognising patterns in multivariate time-series data.

Publications include:

• N. Jamali and C. Sammut. Slip prediction using hidden markov models: Multidimensional sensor data to symbolic temporal pattern learning. In IEEE International Conference on Robotics and Automation, 2012.
• N. Jamali and C. Sammut. Majority voting: Material classification by tactile sensing using surface texture. IEEE Transactions on Robotics, vol. 27, no. 3, pp.508–521, 2011.