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Reinforcement Learning for 4-Finger-Gripper Manipulation

Ojer de Andrés, Marco; Ghazaei Ardakani, Mahdi LU and Robertsson, Anders LU (2018) International Conference on Robotics and Automation (ICRA) 2018
Abstract
In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the parameters of policies, represented by rhythmic Dynamic Movement Primitives (DMPs). The paper studies the case of a 4-finger-gripper manipulator, which performs the task of continuously spinning a ball around a desired axis. The results demonstrate the efficacy of the hierarchical planning and the improvement obtained in the performance of the task... (More)
In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the parameters of policies, represented by rhythmic Dynamic Movement Primitives (DMPs). The paper studies the case of a 4-finger-gripper manipulator, which performs the task of continuously spinning a ball around a desired axis. The results demonstrate the efficacy of the hierarchical planning and the improvement obtained in the performance of the task when PI^2 is used in conjunction with rhythmic DMPs in a real environment. (Less)
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author
organization
publishing date
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Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of 2018 IEEE International Conference on Robotics and Automation
conference name
International Conference on Robotics and Automation (ICRA) 2018
conference location
Brisbane, Australia
conference dates
2018-05-21 - 2018-05-25
language
English
LU publication?
yes
id
1d8fbbc4-d89b-451b-995d-53dae6d6c3a5
date added to LUP
2018-05-17 13:42:23
date last changed
2018-11-21 21:39:51
@inproceedings{1d8fbbc4-d89b-451b-995d-53dae6d6c3a5,
  abstract     = {In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the parameters of policies, represented by rhythmic Dynamic Movement Primitives (DMPs). The paper studies the case of a 4-finger-gripper manipulator, which performs the task of continuously spinning a ball around a desired axis. The results demonstrate the efficacy of the hierarchical planning and the improvement obtained in the performance of the task when PI^2 is used in conjunction with rhythmic DMPs in a real environment.},
  author       = {Ojer de Andrés, Marco and Ghazaei Ardakani, Mahdi and Robertsson, Anders},
  language     = {eng},
  location     = {Brisbane, Australia},
  title        = {Reinforcement Learning for 4-Finger-Gripper Manipulation},
  year         = {2018},
}