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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 In Proceedings of 2018 IEEE International Conference on Robotics and Automation
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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Proceedings of 2018 IEEE International Conference on Robotics and Automation
conference name
International Conference on Robotics and Automation (ICRA) 2018
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English
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yes
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1d8fbbc4-d89b-451b-995d-53dae6d6c3a5
date added to LUP
2018-05-17 13:42:23
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2018-06-13 11:17:42
@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},
  booktitle    = {Proceedings of 2018 IEEE International Conference on Robotics and Automation},
  language     = {eng},
  title        = {Reinforcement Learning for 4-Finger-Gripper Manipulation},
  year         = {2018},
}