Touch if it's transparent! ACTOR: Active Tactile-based Category-Level Transparent Object Reconstruction
Prajval Kumar Murali, Bernd Porr, Mohsen Kaboli

TL;DR
ACTOR introduces a novel active tactile-based framework for accurate shape reconstruction and pose estimation of transparent objects, overcoming vision limitations through self-supervised learning and efficient exploration strategies.
Contribution
The paper presents ACTOR, a new framework combining self-supervised learning and active tactile exploration for category-level transparent object reconstruction and pose estimation.
Findings
Outperforms state-of-the-art methods in tactile-based reconstruction
Effective in reconstructing unknown transparent objects
Improves pose estimation accuracy for transparent objects
Abstract
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…
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Taxonomy
TopicsTactile and Sensory Interactions · Advanced Sensor and Energy Harvesting Materials · Robot Manipulation and Learning
