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Manipulation by Feel: Touch-Based Control with Deep Predictive Models

Authors :
Tian, Stephen
Ebert, Frederik
Jayaraman, Dinesh
Mudigonda, Mayur
Finn, Chelsea
Calandra, Roberto
Levine, Sergey
Publication Year :
2019

Abstract

Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: https://sites.google.com/view/deeptactilempc<br />Comment: Accepted to ICRA 2019

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1903.04128
Document Type :
Working Paper