Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network
This addresses slip detection for industrial robotics to improve manipulation stability, but it is incremental as it applies existing learning methods to a specific sensor type.
The paper tackles slip detection in robotics using barometric tactile sensors and a temporal convolutional neural network, achieving high detection accuracies and robustness to slip speed and direction, with successful generalization to real-world manipulation tasks.
The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, off-the-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.