Researchers at OpenAI — a well-known San Francisco-based research lab focused on developing benevolent artificial intelligence — recently announced they’d done just that, achieving a new robotics benchmark in an era of increasingly sophisticated intelligent machines.
In a statement hailing their work, researchers said the robotic hand, which they’ve dubbed Dactyl, moves robots one step closer to “human-level dexterity.”
“Solving a Rubik’s Cube requires unprecedented dexterity and the ability to execute flawlessly or recover from mistakes successfully for a long period of time,” the statement said. “Even for humans, solving a Rubik’s Cube one-handed is no simple task — there are 43,252,003,274,489,856,000 ways to scramble a Rubik’s Cube.”
With this result, the statement added, researchers move closer to creating “general purpose robots with a technique that should allow for robustly solving any simulatable dexterous tasks.”
The multicolored, three-dimensional puzzles have befuddled game-playing humans since the 1970s, but Rubik’s Cubes have more recently proved a useful tool for measuring the capabilities of artificial intelligence.
For years now, some researchers have been programming robots to solve Rubik’s Cubes as quickly as possible. But more recently, they’ve begun prioritizing self-learning over speed. In July, the University of California at Irvine announced that an artificial intelligence system solved a Rubik’s cube in just over a second, besting the current human world record by more than two seconds.
For some researchers, like those at the University of California Irvine, the cube has offered a novel way to challenge an artificially-intelligent algorithm. That’s because there are billions of potential moves available to a Rubik’s Cube player, with the puzzle’s six sides and nine sections, but only one goal: each of the cube’s six sides displaying a solid color. Finding a solution to a puzzle with that degree of complexity, and among billions of potentialities, involves a degree of abstract thinking, UC Irvine researchers say, that begins to approximate human reasoning and decision-making.
In contrast, OpenAI’s robotic hand wasn’t engineered for the specific purpose of solving Rubik’s Cubes, but rather on physical manipulation of the puzzle.
The UC Irvine system, known as DeepCubeA — a reinforcement-learning algorithm programmed by computer scientists and mathematicians at Irvine— solved the puzzle without prior knowledge of the game or coaching from its human handlers, according to the university.
Highly skilled humans are able to conquer a Rubik’s Cube in about 50 moves, but the AI system is able to solve the cube in about 20, usually in the minimum number of steps possible, researchers said.
The Irvine algorithm relies on a neural network — a set of algorithms designed to find underlying relationships by mimicking how the human brain processes information. The algorithm also relied on machine learning techniques, a system that allows AI to learn by identifying patterns and using inference with minimal human intervention.
To prepare Dactyl for Rubik’s Cube success, OpenAI’s researchers say they didn’t “explicitly program” the machine to solve the puzzle. Instead, the robot was trained using virtual simulations before it was presented with challenges in the physical world that tested its ability to learn.
The goal, researchers say, was to create a robot that learns the way humans do — through trial and error. Eventually, those robots could be used to complete tasks — in a warehouse or perhaps on the surface of another planet — with more autonomy. Before it could solve the puzzle, Dactyl was forced to learn how to hold and move the cube on its own. As Dactyl improved at each stage of learning, its algorithm growing more adept, the challenges intensified.
“For example,” researchers said, “we put a rubber glove on the hand, we tied some of its fingers together, we used a blanket to occlude and perturb the hand, and we poked the Rubik’s Cube with different objects all while it continued to try to solve the Rubik’s Cube.”
The system had never seen anything similar to these situations during training, researchers added.
OpenAI posted a video on YouTube showing Dactyl at various points in the robot’s training arc. The video captures the machine learning from scratch as it awkwardly fumbles with a Rubik’s Cube and later handling the puzzle with much more control and precision.
The video’s narrator says Dactyl’s accomplishment could also change how researchers view training general-purpose robots. Instead of thinking about creating complex algorithms for different environments, the narrator says, roboticists can instead focus on designing complex scenarios in which the machines can learn.
“At some point,” the narrator adds, “then it would be more down to the imagination what robots could actually accomplish.”
“The hope is to build robots that can do many different tasks to increase the standard of living and give everybody a better life,” the narrator adds.
Correction: OpenAI focused their research on physical manipulation of a Rubik’s Cube using a robotic hand, not on solving the puzzle. A previous version of this article suggested that researchers were using software to solve the Rubik’s Cube as other researchers have done in the past. The reference has now been removed.