A robot that learns from its mistakes is a robot that can conquer any environment. Reinforcement learning is the science of teaching through experience.
1The Loop of Experience
Reinforcement Learning is based on a simple cycle. The Agent (robot) observes the State (sensor data), chooses an Action (motor commands), and receives a Reward. The goal of the algorithm is to find a Policy (a mapping from state to action) that maximizes the 'Cumulative Reward'. For a legged robot, the reward might be 'Distance traveled forward' minus 'Penalty for falling'. Through millions of iterations, the robot 'Discovers' that a specific walking gait is the most efficient way to get that reward.
2Reward Shaping
The most difficult part of robotic RL is Reward Shaping. If you only give a reward when the robot reaches the finish line, it might never find it by random chance (Sparse Reward). Instead, we give 'Breadcrumbs'—small rewards for moving in the right direction, keeping a stable posture, or saving energy. However, you must be careful: if the reward is too high for 'staying upright', the robot might decide to never move at all! This is called Reward Hacking.
3Crossing the Reality Gap
Training a physical robot takes thousands of hours and would likely result in the robot breaking itself. We solve this with Sim-to-Real. We use massive physics engines like PyBullet or NVIDIA Isaac Gym to train the robot's policy in a virtual world. To ensure the policy works in the real world (overcoming the 'Reality Gap'), we use Domain Randomization—randomly changing the gravity, friction, and mass in the simulation so the robot learns to be robust to any physical environment.
