Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations
Name
1803.01940.pdf
Description
Accepted version
Size
6.02 MB
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Unknown
Checksum (MD5)
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Author(s)
Hogan, Francois R.
Bauza Villalonga, Maria
Canal, Oleguer
Donlon, Elliott S
Rodriguez, Alberto
Date Issued
October 2018
Journal
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Citation
Hogan, Francois R., Bauza, Maria, Canal, Oleguer, Donlon, Elliott and Rodriguez, Alberto. 2018. "Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations." 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Version
Author's final manuscript
Abstract
This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments.Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the quality of the grasp.First, we construct a tactile-based grasp quality metricusing a deep convolutional neural network trained on over2800 grasps. The quality of each grasp, a continuous value between 0 and 1, is determined experimentally by measuring its resistance to external perturbations. Second, we simulate the tactile imprints associated with robot motions relative to the initial grasp by performing rigid-body transformations of the given tactile measurements. The newly generated tactile imprints are evaluated with the learned grasp quality network and the regrasp action is chosen to maximize the grasp quality.Results show that the grasp quality network can predict the outcome of grasps with an average accuracy of 85%on known objects and 75%on novel objects. The regrasp control policy improves the success rate of grasp actions by an average relative increase of 70%on a test set of 8 objects. We provide a video summarizing our approach at https://youtu.be/gjn7DmfpwDk.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2018.8593528
