Welcome to the fascinating world of humanoid robotics! In this article, we’ll take a journey through the code structure and setup needed to help your humanoid robot take its very first steps, equipped with the ability to navigate planned footsteps and react to its current environment. Let’s dive in!

Understanding the Code Structure

The codebase is designed to be modular, making it easy to customize for your specific humanoid robot. Think of it like a human body where various components have their distinct functions, yet they all work together to achieve walking.

• envs: This is where the action takes place! It contains details about actions, observation spaces, proportional-derivative (PD) gains, simulation steps, and initial conditions.
• tasks: Just like creating a game, this section defines the rules—reward functions, termination conditions, and more!
• rl: The brain of your robot—this contains the code that employs Proximal Policy Optimization (PPO) and actor-critic methods for managing how your robot learns and adapts.
• models: Visual representations in MuJoCo’s XML format, including meshes and textures to construct your robot’s simulation environment.
• trained: Here lie the treasures—pretrained models that allow your robot to get started without the need to learn everything from scratch.
• scripts: Utility scripts to aid you in the journey, helping with various enhancing functions.

Setting Up Your Environment

Before you unleash your robot into action, you need the right setup. Here’s how you can get started:

• Ensure you’re running Python version 3.7.11.
• Install the required libraries:
• Pytorch
• pip install mujoco==2.2.0
• mujoco-python-viewer
• pip install ray==1.9.2
• pip install transforms3d
• pip install matplotlib
• pip install scipy

Bringing Your Robot to Life

Training Mode

To train your humanoid robot, commence the training process using the following command line:

$ python run_experiment.py train --logdir path_to_exp_dir --num_procs num_of_cpu_procs --env name_of_environment

Playing With Your Robot

If you want to see your robot in action, you’ll need to execute a specific script for each environment. For instance, if you’re utilizing the jvrc_step environment, you can use debug_stepper.py:

$ PYTHONPATH=.:$PYTHONPATH python scripts/debug_stepper.py --path path_to_exp_dir

What You Should Witness

Your hard work will pay off when you see your robot ascending stairs, descending, and confidently walking on curves!

• Ascending Stairs: 
• Descending Stairs: 
• Walking on Curves: 

Troubleshooting

If you encounter any hurdles during this process, here are some troubleshooting ideas:

• Make sure all Python libraries are installed correctly and compatible versions are used.
• Double-check your environment paths to ensure they are accurate.
• If scripts fail to execute, scrutinize error messages carefully; they often provide hints for what went wrong.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.