Experimental study on model- vs. learning-based slip detection

Abstract

Vision and proprioception are traditional sources of information for robotic grasping, but they are insufficient to achieve a stable grasp without slippage or without applying an excessive force on the object. Tactile sensors can aid in this problem by providing spatial and temporal data on the contact between fingertips and object. In this work, tactile fingertip sensors are used to detect slippage through two separate methods: the first, using principles inspired by human tactile sensing, and the second, by using a convolutional neural network trained with suitably labeled test samples. To perform a fair comparison of the methods, two evaluations are performed using a test bench and a pick-and-place robotic application. Results show promising use of the model-based method to avoid translational slippage, as it was able to consistently keep objects from slipping without overloading the grasp. Limitations of both model- and learning-based approaches are identified and discussed.