One reason, researchers say: 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 that, researchers say, begins to approximate human reasoning and decision-making.
Yesterday, artificial intelligence(AI) powerhouse OpenAI astonished the world by unveiling a prototype of a robotic arm that could solve a Rubik's cube with one hand. The prototype didn't only represent a milestone for the robotics ecosystem in solving high complexity tasks that actively require sensorial information but it also resulted on a major achievement for the AI community. The reason is that the OpenAI robot was completely trained using simulations based on the reinforcement learning models that the OpenAI Five system used to beat human players in Dota2. The research was discussed in a paper that accompanied the news. The importance of OpenAI's achievement was not about designing a robot that could solve a Rubik's cube.
New research shows that monkeys outperform humans in a test meant to measure cognitive flexibility. The experiment, conducted by a team of psychology researchers at George State University, pitted humans against capuchin and rhesus macaque monkeys. Both groups were asked to interact with a touchscreen computer that featured four squares with different patterns in them. When subjects pressed on the squares in the right sequence, a triangle would appear in place of one of the squares, and when pressed the triangle would produce a reward. For the monkeys, the reward was a banana pellet, and for humans it was either a short audio jingle or a sign of points being tallied up.
DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state -- each of six sides displaying a solid color -- which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban. "Artificial intelligence can defeat the world's best human chess and Go players, but some of the more difficult puzzles, such as the Rubik's Cube, had not been solved by computers, so we thought they were open for AI approaches," said senior author Pierre Baldi, UCI Distinguished Professor of computer science.
Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says that Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. 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 a human speedcuber solves the cube in a matter of seconds.
To avoid this, roboticists use simulation: they build a virtual model of their robot and train it virtually to do the task at hand. The algorithm learns in the safety of the digital space and can be ported into a physical robot afterwards. But that process comes with its own challenges. It's nearly impossible to build a virtual model that exactly replicates all the same laws of physics, material properties, and manipulation behaviors seen in the real world--let alone unexpected circumstances. Thus, the more complex the robot and task, the more difficult it is to apply a virtually trained algorithm in physical reality.
A robotic hand with human-like fingers has solved a Rubik's cube in around three minutes. The machine, guided by artificial intelligence, is the first to have managed the feat without being designed specifically for the purpose and to have taught itself. It is built in a way which means it could be used for other things and learnt through a trial-and-error technique known as reinforcement learning. OpenAI taught AI to control the robotic hand which had been developed by the Shadow Robot Company. The AI controlled robotic hand (pictured above) was able to solve the Rubik's cube in three minutes One of the researchers said that the process starts from the very beginning as the AI has to learn how to move the hand.
"This is an interesting and positive step forward, but it is really important not to exaggerate it," said Ken Goldberg, a professor at the University of California, Berkeley, who explores similar techniques. A robot that can solve a Rubik's Cube is not new. Researchers previously designed machines specifically for the task -- devices that look nothing like a hand -- and they can solve the puzzle in less than a second. But building devices that work like a human hand is a painstaking process in which engineers spend months laying down rules that define each tiny movement. The OpenAI project was an achievement of sorts because its researchers did not program each movement into their robotic hand.