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Repository for Final Design, "Motion Control Method for Small Quadruped Robots Based on Trajectory Modulation and Reinforcement Learning" (基于轨迹调制和强化学习的小型四足机器人运动控制方法)
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RS2002/Robot-mouse
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Article: 基于轨迹调制和强化学习的小型四足机器人运动控制方法, Motion Control Method for Small Quadruped Robots Based on Trajectory Modulation and Reinforcement Learning
Author: Zijian Zhao
Supervisors: Prof. Kai Huang, Prof. Gang Chen, Zitao Zhang
The official GitHub for the conference version isRS2002/ARS-Bezier: Official Repository for The Paper, ARS-Bezier: A Trajectory-based Reinforcement Learning Approach for Autonomous Locomotion of a Rat Robot (github.com).
To train the model, use the following command. You can also use--help to find useful parameters such as learning rate.
python train.py --modelPath<environment path>
If you want to use the ARS-Bezier (simple) method, you can runtrain_simple.py in the same way.
To evaluate your model, you can use:
python eval.py --modelPath<environment path> --parameterPath<parameter path>
or
python eval_simple.py --modelPath<environment path> --parameterPath<parameter path>
To train the model in a random environment, you can use therandom_env function inenvironment.py and set the--modelPath parameter as./models/my_test.xml.
Other models like PPO and ETG-RL are located in the "discard" folder, but they may have some bugs.
