OpenAI Inc. demonstrated a one-handed robot solving a Rubik's Cube. Apparently the real breakthrough in this milestone was teaching the system to do the task in simulation. "While the video makes it easy to focus on the physical robot, the magic is mostly happening in simulation, and transferring things learned in simulation to the real world," writes Evan Ackerman in IEEE Spectrum:
The researchers point out that the method they've developed here is general purpose, and you can train a real-world robot to do pretty much any task that you can adequately simulate. You don't need any real-world training at all, as long as your simulations are diverse enough, which is where the automatic domain randomization comes in. The long-term goal is to reduce the task specialization that's inherent to most robots, which will help them be more useful and adaptable in real-world applications.
