OpenAI, a nonprofit, San Francisco-based AI research company backed by Elon Musk, Reid Hoffman, and Peter Thiel, among other titans of industry, made headlines in June when it announced that the latest version of its Dota 2-playing AI -- dubbed OpenAI Five -- managed to beat amateur players. Today, it unveiled another first: a robotics system that can manipulate objects with humanlike dexterity. In a forthcoming paper ("Dexterous In-Hand Manipulation"), OpenAI researchers describe a system that uses a reinforcement model, where the AI learns through trial and error, to direct robot hands in grasping and manipulating objects with state-of-the-art precision. All the more impressive, it was trained entirely digitally, in a computer simulation, and wasn't provided any human demonstrations by which to learn. "While dexterous manipulation of objects is a fundamental everyday task for humans, it is still challenging for autonomous robots," the team writes.

That's how much virtual computing time it took researchers at OpenAI, the non-profit artificial intelligence lab funded by Elon Musk and others, to train its disembodied hand. The team paid Google $3,500 to run its software on thousands of computers simultaneously, crunching the actual time to 48 hours. After training the robot in a virtual environment, the team put it to a test in the real world.

Gripping something with your hand is one of the first things you learn to do as an infant, but it's far from a simple task, and only gets more complex and variable as you grow up. This complexity makes it difficult for machines to teach themselves to do, but researchers at Elon Musk and Sam Altman-backed OpenAI have created a system that not only holds and manipulates objects much like a human does, but developed these behaviors all on its own. Many robots and robotic hands are already proficient at certain grips or movements -- a robot in a factory can wield a bolt gun even more dexterously than a person. But the software that lets that robot do that task so well is likely to be hand-written and extremely specific to the application. You couldn't for example, give it a pencil and ask it to write.

A new system has vastly improved robots' abilities to grip, slide and manipulate objects with almost the same ease as a human hand. OpenAI, a robotics research group that's backed by tech titans including Elon Musk and Peter Thiel, trained the robot hand to be able to manipulate objects using a sophisticated system called Dactyl. Researchers let a computer simulation of a robot hand learn new movements via trial and error, which served as the dataset for the actual robot hand - meaning it required zero human intervention. Researchers at OpenAI first trained a virtual hand, powered by a neural network, to learn how to manipulate a cube using various grasps. Via simulations, the virtual hand could try out thousands of different poses in just a few seconds.

Vid Human hands are surprisingly dexterous: they can knit clothes, stuff delivery packages with things, play the piano, and so on, albeit with practice. Yet if you're worried machines are going to take these pleasures away from us, be assured us mortals can, for now, pick up these skills faster than robots can, judging from the following findings. Researchers at OpenAI trained, using about a hundred years of simulated experience, a robotic system called Dactyl to rotate and orientate a cube. Dactyl exists not just in its virtual world, though. It can also control a Shadow Dexterous Hand: a metal meathook complete with five fingers, force sensors, and 24 degrees of freedom – pretty close to a human's 27 degrees of freedom. The cube it's told to fondle features a specific letter and color on each of its six faces, and it has to figure out how to manipulate the object so that it finds the requested symbol.

It's still early days in creating the kind of human-like androids we see in the movies, but new research brings us ever closer to the idea. Boston Dynamics has become the de facto image of locomotion for both humans and their pets, while LG already has its CLOi porter'bots and DARPA is working on centaur-like designs for disaster relief. Now, researchers at the Elon Musk-founded OpenAI are working on making robot hands more dextrous. According to a blog post, the team has trained a human-like robot hand called the Shadow Dextrous Hand to manipulate real-world objects like a child's block. It uses the same algorithms and code from its OpenAI Five project, which has been training DOTA 2 bots to play video games.

In-hand manipulation is one of those things that's fairly high on the list of "skills that are effortless for humans but extraordinarily difficult for robots." Without even really thinking about it, we're able to adaptively coordinate four fingers and a thumb with our palm and friction and gravity to move things around in one hand without using our other hand--you've probably done this a handful (heh) of times today already, just with your cellphone. It takes us humans years of practice to figure out how to do in-hand manipulation robustly, but robots don't have that kind of time. Learning through practice and experience is still the way to go for complex tasks like this, and the challenge is finding a way to learn faster and more efficiently than just giving a robot hand something to manipulate over and over until it learns what works and what doesn't, which would probably take about a hundred years. Rather than wait a hundred years, researchers at OpenAI have used reinforcement learning to train a convolutional neural network to control a five-fingered Shadow hand to manipulate objects, all in just 50 hours.

Elon Musk is kinda worried about AI. ("AI is a fundamental existential risk for human civilization and I don't think people fully appreciate that," as he put it in 2017.) So he helped found a research nonprofit, OpenAI, to help cut a path to "safe" artificial general intelligence, as opposed to machines that pop our civilization like a pimple. Yes, Musk's very public fears may distract from other more real problems in AI. But OpenAI just took a big step toward robots that better integrate into our world by not, well, breaking everything they pick up. OpenAI researchers have built a system in which a simulated robotic hand learns to manipulate a block through trial and error, then seamlessly transfers that knowledge to a robotic hand in the real world.