AI-powered robot solves a Rubik's cube one-handed
The Verge has reported that AI research organisation OpenAI has achieved a new milestone in its quest to build general-purpose, self-learning robots. The group’s robotics division said Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik’s cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome.
In a demonstration video, the robotic hand can be seen fumbling its way towards a complete cube solve with clumsy yet accurate manoeuvres. It takes many minutes, but Dactyl is eventually able to solve the puzzle. It’s somewhat unsettling to see in action, if only because the movements look noticeably less fluid than human ones and especially disjointed when compared to the blinding speed and raw dexterity on display when human speedcubers do it.
But for OpenAI, Dactyl’s achievement brings it one step closer to a much sought-after goal for the broader AI and robotics industries: a robot that can learn to perform a variety of real-world tasks, without having to train for months to years of real-world time and without needing to be specifically programmed.
Machines have been able to solve the Rubik’s cube at superhuman speeds for years, but this case is different.
“Plenty of robots can solve Rubik’s cubes very fast. The important difference between what they did there and what we’re doing here is that those robots are very purpose-built,” said Peter Welinder, a research scientist and robotics lead at OpenAI. “Obviously there’s no way you can use the same robot or same approach to perform another task. The robotics team at OpenAI have very different ambitions. We’re trying to build a general-purpose robot. Similar to how humans and how our human hands can do a lot of things, not just a specific task, we’re trying to build something that is much more general in its scope.”
OpenAI’s Dactyl robot and the software that powers it are much different in design and purpose than a dedicated cube-solving machine. As Welinder said, OpenAI’s ongoing robotics work is not aimed at achieving superior results in narrow tasks, as that only requires you to develop a better robot and program it accordingly. That can be done without modern artificial intelligence.
Instead, Dactyl is developed from the ground up as a self-learning robotic hand that approaches new tasks much like a human would. It’s trained using software that tries, in a rudimentary way at the moment, to replicate the millions of years of evolution that help us learn to use our hands instinctively as children. That could one day, OpenAI hopes, help humanity develop the kinds of humanoid robots we know only from science fiction, robots that can safely operate in society without endangering us and perform a wide variety of tasks in environments as chaotic as city streets and factory floors.
To learn how to solve a Rubik’s cube one-handed, OpenAI did not explicitly program Dactyl to solve the toy, and also chose not to program individual motions for the hand to perform, as it wanted it to discern those movements on its own. Instead, the robotics team gave the hand’s underlying software the end goal of solving a scrambled cube and used modern AI — specifically a brand of incentive-based deep learning called reinforcement learning — to help it along the path towards figuring it out on its own.
Dactyl is a 24-joint robotic hand modelled after a human hand, instead of the claw or pincer style robotic grippers you see in factories. And for the software that powers Dactyl to learn how to utilise all of those joints in a way a human would, OpenAI put it through thousands of years of training in simulation before trying the physical cube solve.
“If you’re training things on the real-world robot, obviously whatever you’re learning is working on what you actually want to deploy your algorithm on. In that way, it’s much simpler. But algorithms today need a lot of data. To train a real-world robot to do anything complex, you need many years of experience,” Welinder said. “Even for a human, it takes a couple of years, and humans have millions of years of evolution to have the learning capabilities to operate a hand.”
OpenAI sees Dactyl’s newly acquired skill as equally important for both the advancement of robotic hardware and AI training. Even the most advanced robots in the world, like the humanoid and dog-like bots developed by industry leader Boston Dynamics, cannot operate autonomously, and they require extensive task-specific programming and frequent human intervention to carry out even basic actions.
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