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Variable Impedance Skill Learning for Contact-Rich Manipulation

Yang, Quantao ; Dürr, Alexander LU orcid ; Topp, Elin Anna LU orcid ; Stork, Johannes and Stoyanov, Todor (2022) In IEEE Robotics and Automation Letters 7(3). p.8391-8398
Abstract
Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later domain transfer even for simple skills involving contact. In this paper, we address the problem of learning contact-rich manipulation policies by extending an existing skill-based RL framework with a variable impedance action space. Our method leverages a small set of suboptimal demonstration trajectories and learns from both position, but also... (More)
Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later domain transfer even for simple skills involving contact. In this paper, we address the problem of learning contact-rich manipulation policies by extending an existing skill-based RL framework with a variable impedance action space. Our method leverages a small set of suboptimal demonstration trajectories and learns from both position, but also crucially impedance-space information. We evaluate our method on a number of peg-in-hole task variants with a Franka Panda arm and demonstrate that learning variable impedance actions for RL in Cartesian space can be deployed directly on the real robot, without resorting to learning in simulation. (Less)
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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Machine learning for Robot Control, Reinforcement Learning, Variable Impedance Control
in
IEEE Robotics and Automation Letters
volume
7
issue
3
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85133737407
ISSN
2377-3766
DOI
10.1109/LRA.2022.3187276
project
Reinforcement Learning in Continuous Spaces with Interactively Acquired Knowledge-based Models
Robot Skill Learning based on Interactively Acquired Knowledge-based Models
language
English
LU publication?
yes
id
0d10c436-e705-4ed3-8748-87f92ff3588e
date added to LUP
2022-09-06 13:39:50
date last changed
2023-11-21 11:03:00
@article{0d10c436-e705-4ed3-8748-87f92ff3588e,
  abstract     = {{Contact-rich manipulation tasks remain a hard problem in robotics that requires interaction with unstructured environments. Reinforcement Learning (RL) is one potential solution to such problems, as it has been successfully demonstrated on complex continuous control tasks. Nevertheless, current state-of-the-art methods require policy training in simulation to prevent undesired behavior and later domain transfer even for simple skills involving contact. In this paper, we address the problem of learning contact-rich manipulation policies by extending an existing skill-based RL framework with a variable impedance action space. Our method leverages a small set of suboptimal demonstration trajectories and learns from both position, but also crucially impedance-space information. We evaluate our method on a number of peg-in-hole task variants with a Franka Panda arm and demonstrate that learning variable impedance actions for RL in Cartesian space can be deployed directly on the real robot, without resorting to learning in simulation.}},
  author       = {{Yang, Quantao and Dürr, Alexander and Topp, Elin Anna and Stork, Johannes and Stoyanov, Todor}},
  issn         = {{2377-3766}},
  keywords     = {{Machine learning for Robot Control; Reinforcement Learning; Variable Impedance Control}},
  language     = {{eng}},
  month        = {{06}},
  number       = {{3}},
  pages        = {{8391--8398}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Robotics and Automation Letters}},
  title        = {{Variable Impedance Skill Learning for Contact-Rich Manipulation}},
  url          = {{http://dx.doi.org/10.1109/LRA.2022.3187276}},
  doi          = {{10.1109/LRA.2022.3187276}},
  volume       = {{7}},
  year         = {{2022}},
}