  In this paper, we investigate the effectiveness of contrastive learning
methods for predicting grasp outcomes in an unsupervised manner. By utilizing a
publicly available dataset, we demonstrate that contrastive learning methods
perform well on the task of grasp outcomes prediction. Specifically, the
dynamic-dictionary-based method with the momentum updating technique achieves a
satisfactory accuracy of 81.83% using data from one single tactile sensor,
outperforming other unsupervised methods. Our results reveal the potential of
contrastive learning methods for applications in the field of robot grasping
and highlight the importance of accurate grasp prediction for achieving stable
grasps.
