FPGA-accelerated sim-to-real control policy learning for robotic arms
File(s)
Accepted version
Author(s)
Guo, Ce
Luk, Wayne
Type
Journal Article
Abstract
Sim-to-real robot learning has been used in various applications, but its implementation in software may not provide the best performance. This tutorial describes how hardware acceleration based on Field-Programmable Gate Array (FPGA) technology for deep reinforcement learning can improve sim-to-real robot control policy learning. A novel architecture for the Deep Deterministic Policy Gradient (DDPG) algorithm is developed for a full-stack sim-to-real development platform to learn control policies for robotic arms. The capability of our development platform is illustrated by transferring learned policies encoded as fixed-point numbers from our implementation to a miniature robotic arm.
Date Issued
2024-03
Date Acceptance
2024-01-01
Citation
IEEE Transactions on Circuits and Systems, Part 2: Express Briefs, 2024, 71 (3), pp.1690-1694
ISSN
1549-7747
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1690
End Page
1694
Journal / Book Title
IEEE Transactions on Circuits and Systems, Part 2: Express Briefs
Volume
71
Issue
3
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
http://dx.doi.org/10.1109/tcsii.2024.3353690
Publication Status
Published
Date Publish Online
2024-01-12