Content Estimation Through Tactile Interactions with Deformable Containers

Pouring snacks and moving containers with beverages are challenging for a service robot. To obtain accurate content properties for planning robotic motion, tactile sensing can provide information about the pressure distribution of the contact surface, which is not obvious by visual observation. In this work, we focus on estimating the content properties of various content materials in distinct deformable containers through tactile interactions. We propose a learning-based model that can estimate content properties by using the tactile data collected by slightly squeezing a container with the content of interest. We analyzed an uncalibrated tactile sensor and collected a dataset consisting of 1125 sets of tactile sequences, which are combinations of five types of deformable containers and eleven types of content materials in different content heights. Experiments were conducted on content estimation with known contents and containers, unknown contents, and unknown containers. For unknown contents, our model can still achieve 8.5% height relative error and 79.7% state of matter accuracy. Furthermore, we analyzed that the tactile features of contents with similar content properties are close in the latent snace to show the effectiveness of our model.