Reinforcement learning techniques applied to the motion planning of a robotic manipulator

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Ribeiro, FM (Author) Other; Pinto, VH (Author) FEUP

Conference proceedings International

Title: IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, Santa Maria da Feira, Portugal, April 29-30, 2022

Pages: 173-178

Indexing

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Other information

Authenticus ID: P-00W-SYZ

DOI: 10.1109/icarsc55462.2022.9784814

Abstract (EN): Throughout this article the execution of the motion planning for a robotic manipulator by means of Reinforcement Learning methods is studied. Towards this, an implementation based on a Wire and loop game is used as an example case to be solved. The loop is controlled in a single plane as the endeffector of the manipulator. The modeling of the problem and the process of training the agent is detailed. This allowed for the verification of the capacity of a learning based method, having produced, under the considered abstractions, satisfying results by gaining the capability of completing the path imposed by the wire in 23 seconds.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6