More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
Name
1805.11085.pdf
Description
Accepted version
Size
5.08 MB
Format
Adobe PDF
Checksum (MD5)
f5b72c2bce5fb125de0799bf5f2448e3
Author(s)
Calandra, Roberto
Owens, Andrew
Jayaraman, Dinesh
Lin, Justin
Yuan, Wenzhen
Malik, Jitendra
Adelson, Edward H
Levine, Sergey
Date Issued
July 2018
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Calandra, Roberto et al. "More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch." IEEE Robotics and Automation Letters 3, 4 (October 2018): 3300 - 3307 © 2016 IEEE
Version
Author's final manuscript
Abstract
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model - a deep, multimodal convolutional network - predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at 1) estimating grasp adjustment outcomes, 2) selecting efficient grasp adjustments for quick grasping, and 3) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2018.2852779
