Shape Related Unknown Object One-Shot Learning Grasping

Grasping unknown objects without re-training is still a challenging task for robot grasping. The traditional methods, including data-driven and transfer-learning based grasp methods still suffered from the extra data labeling, training on new samples. To this end, this paper proposes a method to quickly predict the grasping position of unknown objects. This method uses a Deep Siamese Network (DSN) as the backbone to estimate the similarity between the query object and the support set objects, and designs a new loss function through the shape feature estimation mechanism to learn the correct grasping position of unknown objects. Our method is compared to the state-of-the-art (SOTA) method on a well-known open grasp dataset: Connell Grasp Dataset, and a practical dataset. Experimental results show that the proposed method significantly surpasses the SOTA methods in the one-shot grasping task and has good generalization ability for unknown objects. Given only one unknown object sample, the proposed method can obtain a reliable grasping position of an unknown object without the need to re-collect data for retraining.