Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking
This addresses the challenge of flexible grasping in industrial automation, though it is incremental as it builds on existing model-free and optimization approaches.
The paper tackles the problem of predicting grasp poses for multi-suction cup grippers in robotic bin picking without requiring gripper models or specific training data, achieving effectiveness in real-world industrial evaluations with varying difficulty scenes.
This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper and does not require gripper-specific training data. In particular, we propose a two-step approach, where first, a neural network predicts pixel-wise grasp quality for an input image to indicate areas that are generally graspable. Second, an optimization step determines the optimal gripper selection and corresponding grasp poses based on configured gripper layouts and activation schemes. In addition, we introduce a method for automated labeling for supervised training of the grasp quality network. Experimental evaluations on a real-world industrial application with bin picking scenes of varying difficulty demonstrate the effectiveness of our method.