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Reinforcement Learning for Pivoting Task
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
KTH, School of Computer Science and Communication (CSC), Robotics, perception and learning, RPL.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies without prior knowledge of the dynamics of the task. However, obtaining successful policies required thousands to millions of training episodes, limiting the applicability of these approaches to real hardware. We developed a training procedure that allows us to use a simple custom simulator to learn policies robust to the mismatch of simulation vs robot. In our experiments, we demonstrate that the policy learned in the simulator is able to pivot the object to the desired target angle on the real robot. We also show generalization to an object with different inertia, shape, mass and friction properties than those used during training. This result is a step towards making model-free reinforcement learning available for solving robotics tasks via pre-training in simulators that offer only an imprecise match to the real-world dynamics.

Keyword [en]
Reinforcement Learning, Pivoting, Dexterous Manipulation
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-215138OAI: oai:DiVA.org:kth-215138DiVA, id: diva2:1146611
Note

QC 20171023

Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-10-23Bibliographically approved

Open Access in DiVA

fulltext(5710 kB)9 downloads
File information
File name FULLTEXT01.pdfFile size 5710 kBChecksum SHA-512
06d74b080bbbc4d38ef7866788a59652a16390ee783c675e8f9bf2f693e45b0cc6839304514c6e92baa9012a00121bd70a48ada2d24545afbed5f4eedd496ed9
Type fulltextMimetype application/pdf

Other links

https://arxiv.org/abs/1703.00472

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Antonova, RikaCruciani, SilviaSmith, ChristianKragic, Danica
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
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Output format
  • html
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