Train a Humanoid Robot with AI and Python
3D Simulations and Reinforcement Learning with MuJoCo and Gym
Humanoid robots resemble the human body in shape and movement, designed to work alongside people and interact with tools. Although still emerging, forecasts project billions of humanoid robots by 2050. Leading prototypes today include NEO by 1XTech, Optimus by Tesla, Atlas by Boston Dynamics, and G1 by China’s Unitree Robotics.
Robot Control Methods
Robots can perform tasks through two main methods:
- Manual control: Tasks are explicitly programmed.
- Artificial Intelligence: Robots learn through experience and trial.
Reinforcement Learning (RL) enables robots to discover optimal actions by trial and error, adapting to changing conditions based on rewards and penalties without fixed instructions.
Learning in Simulation
Training real robots through RL is extremely costly, so state-of-the-art methods rely on simulations. This approach speeds up and reduces the cost of data generation. Knowledge is later transferred from simulation to real-world robots, known as the "sim-to-real" or "sim-first" method. It also allows running multiple training sessions in parallel.
Popular 3D Physics Simulators
- PyBullet: Suitable for beginners.
- Webots: For intermediate users.
- MuJoCo: Intended for advanced users.
- Gazebo: Preferred by professionals.
"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."
Author’s summary: Humanoid robot development increasingly relies on reinforcement learning through advanced simulations, enabling adaptive behavior without direct programming and reducing training costs.