Predicting fruit-pick success using a grasp classifier trained on a physical proxy

Apple picking is a challenging manipulation task, but it is difficult to test solutions due to the limited window of time that apples are in season. Previous methods have built simulations of apple trees, but simulations rarely capture soft contact and deformation well, both of which are common in fruit picking. In this paper we present and validate a physical proxy that replicates the mechanics of a real world apple pick. This proxy, in conjunction with a novel hand with multiple sensors, enables large-scale capture of sensor data for data collection and testing. To validate our approach, we train a Long Short-Term Memory network to classify a pick as successful or failed based on sensor feedback from the robot hand. We show that a network trained on the proxy performs as well, or even better, than a network trained solely on real apple trees, with accuracies up to 90%. We determine which sensors are most important for pick classification and also demonstrate that our proxy preserves the most important sensor feature data for pick classification. For our hand, the most informative sensor was the finger's servomotor effort.