Part 4/11:
Tesla is exploring the use of reinforcement learning—a machine learning paradigm where robots improve their performance through trial and error, guided by rewards. Combining this with self-play (robots practicing and refining skills in simulated environments) allows Optimus to increase its reliability in real-world scenarios.
This dual approach—learning from videos and practicing through reinforcement—aims to create robots capable of mastering complex manual tasks like cooking, cleaning, ironing, watering plants, and more. The goal is for Optimus to become more adaptable and efficient, with many behaviors rooted in a single neural network.