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. They are designed to work alongside people and interact with tools. Although this technology is still emerging, forecasts predict billions of humanoid robots by 2050.
Currently, the most advanced prototypes include:
- NEO by 1XTech
- Optimus by Tesla
- Atlas by Boston Dynamics
- G1 by China’s Unitree Robotics
Robot Task Execution: Manual Control vs Artificial Intelligence
Robots can perform tasks in two main ways: manual control, where their actions are specifically programmed, or Artificial Intelligence, where they learn through experience.
Reinforcement Learning "allows a robot to learn the best actions through trial and error to achieve a goal," adapting to new environments by learning from rewards and penalties without a fixed plan.
Since training a real robot is extremely expensive, most advanced methods train robots in simulation environments, where data generation is faster and cheaper. Afterward, this knowledge is transferred to the physical robot, an approach known as “sim-to-real” or “sim-first.” This method allows simultaneous training of multiple models.
Popular 3D Physics Simulators
- PyBullet (for beginners)
- Webots (intermediate level)
- MuJoCo (advanced users)
- Gazebo (professional level)
These simulators provide realistic environments to train humanoid robots efficiently before deploying their learnings to real hardware.
Author’s summary: Training humanoid robots with AI leverages simulation environments and reinforcement learning to efficiently develop adaptive behaviors before applying them to real-world machines.