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Contact localization on grasped objects using tactile sensing

Manipulation tasks often require robots to make contact between a grasped tool and another object in the robot's environment. The ability to detect and estimate the positions and directions of these contact points is crucial for monitoring the progress of the task, and detecting failures. In this paper, we present a data-driven approach for detecting and localizing contacts between a grasped object and the environment using tactile sensing. We explore framing the contact localization as both a regression and a classification problem and train neural networks accordingly to estimate the contact parameters. We also compare the neural networks with Gaussian process regression and support vector machine classification with spatio-temporal hierarchical matching pursuit feature learning. We evaluate the presented approach using hundreds of contact events on eighteen objects with different shapes, sizes and material properties. The experiments show that the neural network approach can learn to localize contact events for individual objects with a mean absolute error of less than 2.5 cm for the positions and less than 10° for the directions.