论文标题
跨模式对象识别的转移学习方法：从视觉观察到机器人触觉探索
A Transfer Learning Approach to Cross-Modal Object Recognition: From Visual Observation to Robotic Haptic Exploration
论文作者
论文摘要
在这项工作中，我们介绍了具有机器人主动探索的跨模式视觉触诊对象识别的问题。在这个术语中，我们的意思是，机器人观察了一组具有视觉感知的对象，后来，它只能通过触觉探索识别此类对象，而没有触摸过任何对象。使用机器学习术语，在我们的应用程序中，我们有一个视觉训练集和触觉测试集，反之亦然。为了解决这个问题，我们提出了一种由四个步骤构成的方法：找到一个视觉仪的通用表示形式，定义了一组合适的功能，将特征传递到整个域中，并分类对象。我们使用一组15个对象显示了方法的结果，为每个对象收集40个视觉示例和5个触觉示例。所提出的方法的准确性为94.7％，与单色情况的准确性相当，即在使用视觉数据作为训练集和测试集时。此外，与人类能力相比，它的表现良好，我们大致估计了对十名参与者进行实验。
In this work, we introduce the problem of cross-modal visuo-tactile object recognition with robotic active exploration. With this term, we mean that the robot observes a set of objects with visual perception and, later on, it is able to recognize such objects only with tactile exploration, without having touched any object before. Using a machine learning terminology, in our application we have a visual training set and a tactile test set, or vice versa. To tackle this problem, we propose an approach constituted by four steps: finding a visuo-tactile common representation, defining a suitable set of features, transferring the features across the domains, and classifying the objects. We show the results of our approach using a set of 15 objects, collecting 40 visual examples and five tactile examples for each object. The proposed approach achieves an accuracy of 94.7%, which is comparable with the accuracy of the monomodal case, i.e., when using visual data both as training set and test set. Moreover, it performs well compared to the human ability, which we have roughly estimated carrying out an experiment with ten participants.
