Abdallah Ayad

Abdallah Ayad, Adrian Röfer, Nick Heppert et al.

Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to predict the expected touch signal based on a visual patch representing the area to be touched. We use ReSkin, an inexpensive and compact touch sensor to collect the required dataset through random touching of five basic geometric shapes, and one tool. We train Imagine2touch on two out of those shapes and validate it on the ood. tool. We demonstrate the efficacy of Imagine2touch through its application to the downstream task of object recognition. In this task, we evaluate Imagine2touch performance in two experiments, together comprising 5 out of training distribution objects. Imagine2touch achieves an object recognition accuracy of 58% after ten touches per object, surpassing a proprioception baseline.

Adrian Röfer, Nick Heppert, Abdallah Ayad et al.

Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike their popular optical counterparts, they are difficult to deploy in high-fidelity tasks due to their low signal dimensionality and lack of a simulation model. To overcome these challenges, we introduce PseudoTouch which links high-dimensional structural information to low-dimensional sensor signals. It does so by learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal. We collect and train PseudoTouch on a dataset comprising aligned tactile and visual data pairs obtained through random touching of eight basic geometric shapes. We demonstrate the utility of our trained PseudoTouch model in two downstream tasks: object recognition and grasp stability prediction. In the object recognition task, we evaluate the learned embedding's performance on a set of five basic geometric shapes and five household objects. Using PseudoTouch, we achieve an object recognition accuracy 84% after just ten touches, surpassing a proprioception baseline. For the grasp stability task, we use ACRONYM labels to train and evaluate a grasp success predictor using PseudoTouch's predictions derived from virtual depth information. Our approach yields a 32% absolute improvement in accuracy compared to the baseline relying on partial point cloud data. We make the data, code, and trained models publicly available at https://pseudotouch.cs.uni-freiburg.de.