Researchers have used neural networks to train a robot hand to solve the Rubik's Cube. The system was trained entirely in simulation using reinforcement learning and a technique called Automatic Domain Randomization (ADR). This approach allows the system to handle unexpected situations, such as being prodded by an object it wasn't trained on. The success of this project suggests that reinforcement learning can be applied to physical-world problems requiring dexterity. --- Why it matters: This matters because it shows that AI techniques developed for virtual tasks can be adapted to solve complex physical-world problems, potentially leading to breakthroughs in robotics and automation. Source: https://openai.com/index/solving-rubiks-cube