Robotic capacities in object manipulation are incomparable to those of humans. Besides years of learning, humans rely heavily on the richness of information from physical interaction with the environment. In particular, tactile sensing is crucial in providing such rich feedback. Despite its potential contributions to robotic manipulation, tactile sensing is less exploited; mainly due to the complexity of the time series provided by tactile sensors. In this work, we propose a method for assessing grasp stability using tactile sensing. More specifically, we propose a methodology to extract task-relevant features and design efficient classifiers to detect object slippage with respect to individual fingertips. We compare two classification models: support vector machine and logistic regression. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method. Our results demonstrate that the proposed method is effective in slippage detection in an online fashion.

翻译：机器人在物体操作方面的能力远不及人类。除了多年的学习，人类还严重依赖与物理环境交互所获得的丰富信息。特别是，触觉传感在提供这种丰富反馈方面至关重要。尽管触觉传感对机器人操作有潜在贡献，但其应用较少，这主要是因为触觉传感器提供的时间序列具有复杂性。在这项工作中，我们提出了一种利用触觉传感评估抓握稳定性的方法。具体而言，我们提出了一种提取任务相关特征并设计高效分类器的方法，用以检测相对于单个指尖的物体滑移。我们比较了两种分类模型：支持向量机与逻辑回归。我们使用安装在Allegro手上的高灵敏度Uskin触觉传感器来测试和验证我们的方法。结果表明，所提出的方法能够有效在线检测滑移现象。