GraspContrast: Self-Supervised Contrastive Learning with False Negative Elimination for 6-DoF Grasp Detection

Robotic manipulation is a grand domain that primarily involves the use of robotic arms to interact with objects in the environment. While proposed methods have achieved advancements in grasping objects, they rely heavily on extensive training data that presents a significant challenge due to the labor-intensive process of human annotation. To address the issue, we propose GraspContrast, a self-supervised contrastive learning framework leveraging unlabeled RGB-D images to enhance point-wise feature representations for 6-DoF grasp detection. Our method designs a dual-branch network architecture to learn transformations that embed positive point pairs nearby, while pushing negative point pairs far apart. Specifically, we discuss a false negative elimination strategy to explicitly detect and remove the false negative samples that undesirably repel the point instances from the geometrically similar samples. Our method exhibits consistent improvements over existing learning-based grasp detection methods on both the GraspNet-1B benchmark and physical UR10e platform. These significant performance gains demonstrate the effective- ness of our proposed framework.