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Accurate shape reconstruction of transparent objects is a challenging taskdue to their non-Lambertian surfaces and yet necessary for robots for accuratepose perception and safe manipulation. As vision-based sensing can produceerroneous measurements for transparent objects, the tactile modality is notsensitive to object transparency and can be used for reconstructing theobject's shape. We propose ACTOR, a novel framework for ACtive tactile-basedcategory-level Transparent Object Reconstruction. ACTOR leverages largedatasets of synthetic object with our proposed self-supervised learningapproach for object shape reconstruction as the collection of real-worldtactile data is prohibitively expensive. ACTOR can be used during inferencewith tactile data from category-level unknown transparent objects forreconstruction. Furthermore, we propose an active-tactile object explorationstrategy as probing every part of the object surface can be sample inefficient.We also demonstrate tactile-based category-level object pose estimation taskusing ACTOR. We perform an extensive evaluation of our proposed methodologywith real-world robotic experiments with comprehensive comparison studies withstate-of-the-art approaches. Our proposed method outperforms these approachesin terms of tactile-based object reconstruction and object pose estimation.

Touch if it's transparent! ACTOR: Active Tactile-based Category-Level Transparent Object Reconstruction