Robust Learning-Based Incipient Slip Detection using the PapillArray
  Optical Tactile Sensor for Improved Robotic Gripping
Qiang Wang Pablo Martinez Ulloa Robert Burke David Cordova Bulens Stephen J. Redmond
The ability to detect slip, particularly incipient slip, enables robotic
systems to take corrective measures to prevent a grasped object from being
dropped. Therefore, slip detection can enhance the overall security of robotic
gripping. However, accurately detecting incipient slip remains a significant
challenge. In this paper, we propose a novel learning-based approach to detect
incipient slip using the PapillArray (Contactile, Australia) tactile sensor.
The resulting model is highly effective in identifying patterns associated with
incipient slip, achieving a detection success rate of 95.6% when tested with an
offline dataset. Furthermore, we introduce several data augmentation methods to
enhance the robustness of our model. When transferring the trained model to a
robotic gripping environment distinct from where the training data was
collected, our model maintained robust performance, with a success rate of
96.8%, providing timely feedback for stabilizing several practical gripping
tasks. Our project website:
https://sites.google.com/view/incipient-slip-detection.