The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results obtained from the data, and this issue is observed more frequently in robotic tactile perception. In tactile perception, due to the limited working space and the dynamic environment, the contact between the tactile sensor and the object is frequently insufficient and unstable, which causes the partial loss of signals, thus leading to incomplete tactile data. The tactile data will therefore contain fewer tactile cues with low information density. In this paper, we propose a tactile representation learning method, named TacMAE, based on Masked Autoencoder to address the problem of incomplete tactile data in tactile perception. In our framework, a portion of the tactile image is masked out to simulate the missing contact region. By reconstructing the missing signals in the tactile image, the trained model can achieve a high-level understanding of surface geometry and tactile properties from limited tactile cues. The experimental results of tactile texture recognition show that our proposed TacMAE can achieve a high recognition accuracy of 71.4% in the zero-shot transfer and 85.8% after fine-tuning, which are 15.2% and 8.2% higher than the results without using masked modeling. The extensive experiments on YCB objects demonstrate the knowledge transferability of our proposed method and the potential to improve efficiency in tactile exploration.

翻译：物体遮挡或传感器不稳定导致的信号缺失是数据采集中的常见挑战。此类信号缺失会严重影响数据分析结果，这一现象在机器人触觉感知中尤为突出。在触觉感知中，由于有限的工作空间和动态环境，触觉传感器与物体之间的接触常存在持续不足与不稳定性，导致信号部分丢失，从而产生不完整的触觉数据。这类触觉数据所含触觉线索较少、信息密度较低。本文提出一种基于掩码自编码器的触觉表征学习方法TacMAE，以解决触觉感知中不完整数据的问题。在框架中，我们掩码部分触觉图像以模拟缺失的接触区域。通过重建触觉图像中的缺失信号，训练后的模型能够从有限的触觉线索中实现表面对几何形状与触觉特性的深度理解。触觉纹理识别实验表明，本文提出的TacMAE在零样本迁移场景下可达71.4%的高识别准确率，微调后可达85.8%，较未使用掩码建模的结果分别提升15.2%和8.2%。在YCB物体上的大量实验证明了该方法的知识可迁移性及其在提升触觉探索效率方面的潜力。