What is imitation learning in robotics?

Imitation learning in robotics is a machine learning method where a robot learns to perform tasks by observing and mimicking the actions of a human or another agent.

Instead of exploring the environment on its own (as in traditional reinforcement learning), the robot receives demonstrations of correct behavior and tries to reproduce them.

Examples of application:

• A robotic manipulator learns to assemble parts by observing an operator.
• An autonomous vehicle learns to drive by analyzing recordings of an experienced driver.

Advantages:

• Rapid adaptation to complex tasks without the need to program each action.
• Ability to utilize expert knowledge for training.

Main techniques include supervised learning, where the model is trained on pairs of 'state — action', and deep learning methods for processing complex data (videos, sensors).