Daily Technology
·05/11/2025
Training a humanoid robot, a machine built to copy human movement, is an enormous task. Writing code for every joint in every possible pose is not realistic. The fix is a branch of artificial intelligence called Reinforcement Learning (RL).
The AI agent receives no step-by-step orders - it tries actions in a simulated world and keeps score. The method is called “sim-to-real” because skills gained in simulation transfer later to a real robot. This keeps training fast, cheap plus safe. A useful action like staying upright, adds points - a harmful action, like falling, subtracts points. The AI's only goal is to collect the highest score possible.
Python libraries and physics engines supply the power. OpenAI Gym offers a standard set of tools for writing RL code. Developers combine it with MuJoCo or similar simulators to build 3D worlds. Inside those worlds, a bipedal robot can live through millions of practice runs in hours instead of months.
Proximal Policy Optimization (PPO) serves as the AI's decision core - it reads data from the simulation - joint angles, speeds - updates its choices after each attempt. No fixed sequence is stored - the network finds the basic rules for balance but also walking on its own.
The outcome surprises many observers. A robot that once waved its limbs and crashed to the floor learns to stand still. Further practice leads to steady walking as well as obstacle avoidance. The lesson marks a clear change in robotics - tomorrow's machines will not need every move programmed - they will need systems smart enough to learn.









