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GCTD3: Modeling of Bipedal Locomotion by Combination of TD3 Algorithms and Graph Convolutional Network
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GIANG NGUYEN,
Dat Nguyen Ngoc
Applied Sciences, Volume: 12, Issue: 6, Start page: 2948
Swansea University Author: GIANG NGUYEN
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DOI (Published version): 10.3390/app12062948
Abstract
In recent years, there has been a lot of research using reinforcement learning algorithms to train 2-legged robots to move, but there are still many challenges. The authors propose the GCTD3 method, which takes the idea of using Graph Convolutional Networks to represent the kinematic link features o...
| Published in: | Applied Sciences |
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| ISSN: | 2076-3417 |
| Published: |
MDPI AG
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71636 |
| Abstract: |
In recent years, there has been a lot of research using reinforcement learning algorithms to train 2-legged robots to move, but there are still many challenges. The authors propose the GCTD3 method, which takes the idea of using Graph Convolutional Networks to represent the kinematic link features of the robot, and combines this with the Twin-Delayed Deep Deterministic Policy Gradient algorithm to train the robot to move. Graph Convolutional Networks are very effective in graph-structured problems such as the connection of the joints of the human-like robots. The GCTD3 method shows better results on the motion trajectories of the bipedal robot joints compared with other reinforcement learning algorithms such as Twin-Delayed Deep Deterministic Policy Gradient, Deep Deterministic Policy Gradient and Soft Actor Critic. This research is implemented on a 2-legged robot model with six independent joint coordinates through the Robot Operating System and Gazebo simulator. |
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| Keywords: |
GCTD3; GCN; TD3; ROS; reward function; bipedal robot |
| College: |
Faculty of Science and Engineering |
| Funders: |
N/A |
| Issue: |
6 |
| Start Page: |
2948 |