This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.

本文提出了一个框架，用于研究机器人触诊过程中物理相互作用对感觉信息的影响。我们在机械臂上嵌入了一个电容式触觉传感器，以探测软体模，并对其中的硬包裹物进行检测和分类。PCA和K-Means聚类的组合用于：首先，减少通过探查体模中每个区域而获得的时空数据的维数；第二，将重新编码的数据分类为给定数量的类别。结果表明，适当的探测交互作用可用于补偿数据质量或数据质量的缺失。最后，我们在支持方案向量机分类器经过训练以区分不同类型的硬质夹杂物的触诊场景下测试提出的框架。