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The purpose of this paper is to analyze surface deformations caused by shear and moment forces on tactile materials and present a method to detect and reduce the risk of slippage by controlling the normal force as measured by tactile sensor arrays.

A predictive model has been proposed which uses a basic method adapted to real applications in grasp optimization. Prevention of premature release with minimum prehension force is addressed without the need to measure the coefficient of friction between object and robot gripper. Predictive models have been used to develop a set of rules which predict the pre‐slip based on fluctuations in tactile signal data.

The tactile sensors can be used in a “nonlinear” manner during manipulation tasks. When the gripper finger first makes contact with an object, the stress distribution under the finger skin varies rapidly. Predictive models have been used to develop a set of rules which predict the pre‐slip based on fluctuations in tactile signal data. Pre‐slip at the contact area just prior to object movement produces rapid but detectable stress transients.

Tactile sensors do not measure stress generated by a contact with an object directly, but instead measure strain in an interposed compliant, polymeric medium intended for sensor protection and prehension assistance. Reliable detection of pre‐slip has hitherto eluded researchers using such tactile techniques.