* Join the Member of ICROS 
* Need your ID or Password?
Subject Keyword Abstract Author
Hierarchical End-to-end Control Policy for Multi-degree-of-freedom Manipulators

Cheol-Hui Min and Jae-Bok Song*
International Journal of Control, Automation, and Systems, vol. 20, no. 10, pp.3296-3311, 2022

Abstract : In recent years, several control policies for a multi-degree-of-freedom (DOF) manipulator using deep reinforcement learning have been proposed. To avoid complexity, previous studies have applied a number of constraints on the high-dimensional state-action space, thus hindering generalized policy function learning. In this study, the control problem is addressed by in-troducing a hierarchical reinforcement learning method that can learn the end-to-end control policy of a multi-DOF manipula-tor without any constraints on the state-action space. The proposed method learns hierarchical policy using two off-policy methods. Using human demonstration data and a newly proposed data-correction method, controlling the multi-DOF manipu-lator in an end-to-end manner is shown to outperform the non-hierarchical deep reinforcement learning methods.

Keyword : Deep reinforcement learning, demonstration-based learning, end-to-end robot control, hierarchical reinforcement learning.

Copyright ⓒ ICROS. All rights reserved.
Institute of Control, Robotics and Systems, Suseo Hyundai-Ventureville 723, Bamgogae-ro 1-gil 10, Gangnam-gu, Seoul 06349, Korea
Homepage | Tel. +82-2-6949-5801 (ext. 3) | Fax. +82-2-6949-5807 | E-mail