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.

翻译：在本文中，我们研究了对比学习方法在无监督方式下预测抓取结果的有效性。通过利用公开数据集，我们证明对比学习方法在抓取结果预测任务上表现良好。具体而言，采用动量更新技术的动态字典方法，利用单一触觉传感器的数据达到了81.83%的令人满意的准确率，优于其他无监督方法。我们的结果揭示了对比学习方法在机器人抓取领域的应用潜力，并强调了准确抓取预测对于实现稳定抓取的重要性。