SCT-CNN: A Spatio-Channel-Temporal Attention CNN for Grasp Stability Prediction
SCT-CNN: A Spatio-Channel-Temporal Attention CNN for Grasp Stability Prediction
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DOI：
10.1109/icra48506.2021.9561397
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Gang Yan;A. Schmitz;Satoshi Funabashi;S. Somlor;Tito Pradhono Tomo;S. Sugano
中科院分区：
文献类型：
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作者：
Gang Yan;A. Schmitz;Satoshi Funabashi;S. Somlor;Tito Pradhono Tomo;S. Sugano
近年来，触觉传感在机器人操作领域引起了人们极大的兴趣。预测抓取是否稳定，即被抓取的物体在被举起时是否会从抓取器中脱落，可以帮助机器人实现健壮的抓取。以往的方法对触觉数据矩阵的所有区域或触觉序列的所有时间步都给予同等的关注，其中可能包含不相关或冗余的信息。在本文中，我们提出在卷积神经网络中配置空间通道和时间注意机制（SCT注意CNN）来预测未来的抓取稳定性。据我们所知，这是第一次仅依靠触觉信息使用注意机制来预测抓取稳定性。我们用52件日常用品进行实验。此外，我们还比较了不同的时空模型和注意机制作为实证研究。我们发现，当使用SCT注意时，准确率显著提高了5%。我们相信，注意机制也可以在未来改善其他触觉学习任务的表现，如滑动检测和硬度感知。
Recently, tactile sensing has attracted great interest for robotic manipulation. Predicting if a grasp will be stable or not, i.e. if the grasped object will drop out of the gripper while being lifted, can aid robust robotic grasping. Previous methods paid equal attention to all regions of the tactile data matrix or all time-steps in the tactile sequence, which may include irrelevant or redundant information. In this paper, we propose to equip Convolutional Neural Networks with spatial-channel and temporal attention mechanisms (SCT attention CNN) to predict future grasp stability. To the best of our knowledge, this is the first time to use attention mechanisms for predicting grasp stability only relying on tactile information. We implement our experiments with 52 daily objects. Moreover, we compare different spatio-temporal models and attention mechanisms as an empirical study. We found a significant accuracy improvement of up to 5% when using SCT attention. We believe that attention mechanisms can also improve the performance of other tactile learning tasks in the future, such as slip detection and hardness perception.