Training a Unitree G1 to Walk w/ Reinforcement Learning

Why watch

A hands-on tutorial demonstrating how to train the Unitree G1 humanoid robot to walk using Proximal Policy Optimization (PPO) reinforcement learning in MuJoCo physics simulation. Covers reward function design, policy architecture, and training dynamics for bipedal locomotion. Ideal for developers and robotics enthusiasts looking to apply RL to real-world humanoid robots.