We present and evaluate a novel suction-based gripper designed for fruit picking. This work is motivated by common problems observed in field trials of robotic harvesting: Calibration/perception errors, workspace obstacles, fruit swinging/moving when contacted, and varying stem and branch stiffnesses. The gripper consists of three suction-cups located on the palm, along with in-hand perception. To evaluate the gripper, we developed a physical proxy that approximates a realistic apple-stem-branch dynamic system. We performed 756 apple picks on the proxy with varying branch stiffness, stem strength and gripper pose (yaw, roll and offset w.r.t. the apple). Our results show that grasping performance improves when the gripper yaw w.r.t. the apple has two suction cups on the bottom of the apple and one suction cup on top. Even with ±15mm offset, at least two suction cups engaged with the apple 80% of the time, regardless of branch stiffness. Moreover, the gripper withstands ±20mm offset when it approaches the apple near its equator.

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.