Variability and predictability in tactile sensing during grasping

Robotic manipulation in unstructured environments requires grasping a wide range of objects. Tactile sensing is presumed to provide essential information in this context, but there has been little work examining the tactile sensor signals produced during realistic manipulation tasks. This paper presents tactile sensor data from grasping a generic object in thousands of trials. Position error between the hand and object was varied to model the uncertainty in real-world grasping, and a grasp outcome prediction was done using only tactile sensors. Results show that tactile signals are highly variable despite good repeatability in grasping conditions. The observed variability appears to be intrinsic to the grasping process, due to the mechanical coupling between fingers as they contact the object in parallel, as well as numerous factors such as frictional effects and inaccuracies in the robot hand. Using a simple machine learning algorithm, grasp outcome prediction based purely on tactile sensors is not reliable enough for real-world responsibilities. These results have implications for improved tactile sensor system and controller design, as well as signal processing and machine learning methods.