Train a Humanoid Robot with AI and Python

3D Simulations and Reinforcement Learning with MuJoCo and Gym

Humanoid robots resemble human bodies in shape and movement, designed to work alongside people and use our tools. Although still emerging, forecasts estimate billions of humanoid robots will exist by 2050.

Leading Prototypes

NEO by 1XTech
Optimus by Tesla
Atlas by Boston Dynamics
G1 by China’s Unitree Robotics

Robot Control Methods

A robot can perform tasks either by manual control, where specific instructions are programmed, or by Artificial Intelligence, where it learns through experience.

Reinforcement Learning

This method enables robots to learn the best actions through trial and error to achieve a goal. It helps robots adapt to changing environments by learning from rewards and penalties without a fixed plan.

Reinforcement Learning allows a robot to learn the best actions through trial and error to achieve a goal, adapting to changing environments by learning from rewards and penalties without a predefined plan.

Simulation-Based Training

Training a real robot is highly expensive, so current best practices involve learning within simulations. This "sim-to-real" approach speeds up data generation and enables simultaneous training of many models before transferring knowledge to actual robots.

Popular 3D Physics Simulators

PyBullet (beginners)
Webots (intermediate)
MuJoCo (advanced)
Gazebo (professionals)

These simulators support rapid and cost-effective development of humanoid robot skills through reinforcement learning techniques.

Author’s summary: Humanoid robots are rapidly evolving through AI-driven reinforcement learning, with simulation environments playing a key role in making their development both practical and scalable.

Towards Data Science — 2025-11-05