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Despite the utility of tactile information, tactile sensors have yet to bewidely deployed in industrial robotics settings. Part of the challenge lies inidentifying slip and other key events from the tactile data stream. In thispaper, we present a learning-based method to detect slip using barometrictactile sensors. Although these sensors have a low resolution, they have manyother desirable properties including high reliability and durability, a veryslim profile, and a low cost. We are able to achieve slip detection accuraciesof greater than 91% while being robust to the speed and direction of the slipmotion. Further, we test our detector on two robot manipulation tasks involvingcommon household objects and demonstrate successful generalization toreal-world scenarios not seen during training. We show that barometric tactilesensing technology, combined with data-driven learning, is potentially suitablefor complex manipulation tasks such as slip compensation.

Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors