A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction

• URL: http://arxiv.org/abs/2306.14437v2
• Date: Thu, 21 Sep 2023 06:54:22 GMT
• Title: A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction
• Authors: Chengliang Liu, Binhua Huang, Yiwen Liu, Yuanzhe Su, Ke Mai, Yupo Zhang, Zhengkun Yi, Xinyu Wu
• Abstract summary: We show that contrastive learning methods perform well on the task of grasp outcomes prediction. Our results reveal the potential of contrastive learning methods for applications in the field of robot grasping.
• Score: 9.865029065814236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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