Acquisition and Prediction of High-Density Tactile Field Data for Rigid and Flexible Objects

摘要

Obtaining high-density tactile field information is a critical aspect of research in the field of robotic haptics, as it plays a decisive role in determining the precision of robot manipulations. Vision-based tactile sensors have unique high-resolution features, which make them promising for related research. However, previous studies have mainly focused on reconstructing the shape of rigid objects or predicting the three-dimensional force of rigid objects, neglecting the analysis of flexible objects. Moreover, due to the resolution limitations of existing commercial sensors, the performance evaluation of previous force prediction models relied solely on the total force. To overcome these limitations and in order to explore the tactile field information of objects with more attributes, this paper presents a detailed high-density tactile field data acquisition method based on a mechanical simulation environment. Additionally, we constructed a network to learn the mapping relationship between tactile images and six-dimensional tactile field information. Our results demonstrate that the proposed method can predict the three-dimensional force and displacement information of the object. Notably, the prediction error is within the tolerance range for fine manipulation by robots.