AgiBot X1 Training Code

This repository contains the training code for teaching AgiBot X1, a modular humanoid robot, how to walk and move. Instead of hand-coding every motion, the developers use reinforcement learning—a machine learning approach where the robot learns by trial and error in a simulation. Once the robot masters walking in the virtual environment, that knowledge can be transferred to the real physical robot.

The workflow is straightforward: you set up a simulated copy of the robot in a physics engine, run the training script to let it practice walking for thousands of iterations, and save the resulting model. The trained model becomes a decision-maker that tells the robot which motors to activate and by how much in order to move smoothly. You can then test the model in the simulator before deploying it to the actual hardware, or even control the robot with a joystick in real time.

The project is built around Isaac Gym, NVIDIA's physics simulation platform that can run many virtual robot copies in parallel—speeding up training dramatically. The repository also includes tools to convert the trained model into formats that the robot's onboard software can use, and a way to validate your trained model in an alternative simulator (Mujoco) before going live. This "sim-to-sim" validation step catches problems early.

Who would use this? Hardware engineers and roboticists working on bipedal or humanoid robots could adapt this codebase to train their own designs. Researchers exploring reinforcement learning for robotics have a working reference implementation. And teams at AgiBot use this to continuously improve X1's locomotion. The repository is open-source, so anyone can inspect how the training loop works, add new robot models, or experiment with different learning algorithms. The main tradeoff is that it requires significant computational resources (a GPU-accelerated machine) and familiarity with Python to get started.