Unitree RL GYM

Unitree robot platform for reinforcement learning

CommonProductProgrammingUnitreeReinforcement Learning

Unitree RL GYM is a reinforcement learning platform based on Unitree robots, supporting models such as Unitree Go2, H1, H1_2, and G1. This platform provides an integrated environment for researchers and developers to train and test reinforcement learning algorithms on real or simulated robots. Its significance lies in promoting the advancement of robot autonomy and intelligent technology, particularly in applications requiring complex decision-making and motion control. Unitree RL GYM is open-source and available for free, mainly targeting researchers and robotics enthusiasts.

Supports various Unitree robot models for reinforcement learning training and testing.
Provides Isaac Gym and Mujoco simulation environments
as well as guidelines for physical robot deployment.
Integrates the PPO reinforcement learning algorithm implementation
facilitating algorithm development and comparison.
Supports custom tasks and experiments
allowing flexible configuration of experimental parameters.
Offers detailed installation and usage documentation for quick user onboarding.
Supports version control for code and algorithms
aiding in experiment reproduction and sharing.

The target audience primarily includes researchers
developers
and students in the fields of robotics
artificial intelligence
and automation. They can utilize Unitree RL GYM to explore and develop advanced robot control algorithms

Researchers use Unitree RL GYM to train walking and balancing algorithms for robots in a simulated environment.
Developers leverage the platform to test the performance of new reinforcement learning algorithms on real robots.
Educational institutions utilize Unitree RL GYM as a teaching tool to demonstrate the fundamental principles of robotic learning and control to students.

1. Create a new Python virtual environment and install the specified version of Python 3.8.
2. Install PyTorch 2.3.1 and the corresponding CUDA version.
3. Download and install Isaac Gym
following the guidelines to run the sample programs.
4. Clone and install rsl_rl (implementation of the PPO algorithm).

Visit

Unitree RL GYM Visit Over Time

Monthly Visits

494758773

Bounce Rate

37.69%

Page per Visit

5.7

Visit Duration

00:06:29

Unitree RL GYM Visit Trend

Unitree RL GYM Visit Geography

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Unitree RL GYM

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