  Accurate shape reconstruction of transparent objects is a challenging task
due to their non-Lambertian surfaces and yet necessary for robots for accurate
pose perception and safe manipulation. As vision-based sensing can produce
erroneous measurements for transparent objects, the tactile modality is not
sensitive to object transparency and can be used for reconstructing the
object's shape. We propose ACTOR, a novel framework for ACtive tactile-based
category-level Transparent Object Reconstruction. ACTOR leverages large
datasets of synthetic object with our proposed self-supervised learning
approach for object shape reconstruction as the collection of real-world
tactile data is prohibitively expensive. ACTOR can be used during inference
with tactile data from category-level unknown transparent objects for
reconstruction. Furthermore, we propose an active-tactile object exploration
strategy as probing every part of the object surface can be sample inefficient.
We also demonstrate tactile-based category-level object pose estimation task
using ACTOR. We perform an extensive evaluation of our proposed methodology
with real-world robotic experiments with comprehensive comparison studies with
state-of-the-art approaches. Our proposed method outperforms these approaches
in terms of tactile-based object reconstruction and object pose estimation.
