PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation

• URL: http://arxiv.org/abs/2403.15107v1
• Date: Fri, 22 Mar 2024 10:51:31 GMT
• Title: PseudoTouch: Efficiently Imaging the Surface Feel of Objects for Robotic Manipulation
• Authors: Adrian Röfer, Nick Heppert, Abdallah Ayman, Eugenio Chisari, Abhinav Valada,
• Abstract summary: Our goal is to equip robots with a similar capability, which we term ourmodel. We frame this problem as the task of learning a low-dimensional visual-tactile embedding. Using ReSkin, we collect and train PseudoTouch on a dataset comprising aligned tactile and visual data pairs. We demonstrate the efficacy of PseudoTouch through its application to two downstream tasks: object recognition and grasp stability prediction.
• Score: 8.997347199266592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term \ourmodel. \ourmodel aims to predict the expected touch signal based on a visual patch representing the touched area. We frame this problem as the task of learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal. To accomplish this task, we employ ReSkin, an inexpensive and replaceable magnetic-based tactile sensor. Using ReSkin, 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 efficacy of PseudoTouch through its application to 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 an impressive 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 http://pseudotouch.cs.uni-freiburg.de.

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