Walking is a fundamental skill for any robotic system, and thanks to advancements in artificial intelligence, specifically Large Language Models (LLMs), we can now instruct robots to walk with incredible efficiency and accuracy. In this blog post, we will explore the steps to set up and run the project titled “Prompt a Robot to Walk with Large Language Models“.

Overview

In our experiments, we utilize an LLM to output low-level actions that enable a robot to walk. The LLM operates at 10 Hz, while the Proportional-Derivative (PD) controller functions at 200 Hz, ensuring that the responsiveness and smoothness of the walking actions are maintained.

Setup

To get started, you need to create a conda environment to manage your dependencies effectively. Here are the steps:

Create Conda Environment

conda create -n prompt2walk python=3.9

conda activate prompt2walk

Install Requirements

After activating the environment, you need to install the necessary requirements. Execute the following command:

pip install -r requirements.txt

Usage

Now that your environment is set up, let’s delve into how to run the project.

Configuration

First, you need to open the srcllm.py file and enter your OpenAI API key. This step is crucial for communication with the LLM.

Collecting the Trajectory from the LLM Controller

Now it’s time to collect the trajectory data that will guide the robot. You can do this by running:

python srcrun.py

Replaying the Trajectory

Once you have the trajectory, it’s time to see it in action. Run the following command:

python srcreplay.py

Training Your Own Policy

If you want to customize your training further, you can refer to the Isaac Gym Environments for Legged Robots. Currently, testing has been conducted with the A1 and ANYmal C Robots at 10 Hz.

Troubleshooting

If you encounter issues throughout the process, consider the following troubleshooting tips:

• Ensure that all packages are correctly installed and up to date by revisiting the requirements file.
• Check your OpenAI API key for typos or truncations.
• Confirm that your system meets the hardware requirements for running the robots.

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Conclusion

By now, you should have a clear understanding of how to get started with prompting a robot to walk using LLMs. This innovative approach paves the way for future advancements in robotics and AI.

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.