Learning to Grasp Without Seeing
Adithyavairavan Murali, Yin Li, Dhiraj Gandhi, Abhinav Gupta

TL;DR
This paper introduces a tactile-sensing based system enabling robots to grasp unknown objects without visual input, utilizing object localization and iterative re-grasping to improve stability, validated on a large dataset with significant accuracy gains.
Contribution
It presents the first learning-based approach for tactile-only grasping of novel objects, combining touch localization and re-grasping with a large-scale tactile dataset.
Findings
Tactile auto-encoding improves perception tasks by 4-9%.
Re-grasping enhances grasp stability and accuracy by 10.6%.
System successfully grasps unseen objects using only tactile sensing.
Abstract
Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9% over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the…
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