OpenAI, the research company that conducts artificial intelligence research was able to train a pair of neural networks to solve the Rubik's Cube using a robotic hand. In a blog post announcing the achievement, OpenAI said the neural networks were trained in simulation, relying on the OpenAIFive code paired with Automatic Domain Randomization, which is a new technique the firm developed. "Human hands let us solve a wide variety of tasks. For the past 60 years of robotics, hard tasks which humans accomplish with their fixed pair of hands have required designing a custom robot for each task. As an alternative, people have spent many decades trying to use general-purpose robotic hardware, but with limited success due to their high degrees of freedom.,"

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 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 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.

Sign in to report inappropriate content. A full solve of the Rubik's Cube. This video plays at real-time and was not edited in any way.

Sign in to report inappropriate content. Perturbations that we apply to the real robot hand while it solves the Rubik's Cube. All videos play at real-time.

We've trained a pair of neural networks to solve the Rubik's Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn't just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity. Human hands let us solve a wide variety of tasks. For the past 60 years of robotics, hard tasks which humans accomplish with their fixed pair of hands have required designing a custom robot for each task.

Sign in to report inappropriate content. We've trained a pair of neural networks to solve the Rubik's Cube with a human-like robot hand.

Artificial intelligence can now solve a Rubik's cube one-handed. The task requires so much dexterity that even humans find the movements difficult. The system was developed by researchers at OpenAI, a technology firm that has previously created an AI that could outplay humans at the video game Dota 2. The team taught an AI to control a commercially available robotic hand developed by the Shadow Robot Company. The AI learned using a technique called reinforcement learning, which involves trial and error. "It starts from not knowing anything about how to move a hand or how a cube would react if you push on the sides or on the faces," says Peter Welinder, part of the team.

There's always been something so annoying about people who found the need to stack additional challenges onto solving a Rubik's Cube quickly, whether it was doing it blind-folded or while juggling or one-handed. While it might have just been a challenge for them, it also seemed like a need to show off. OpenAI is clearly interested in showing off what its Dactyl robotic-hand can do with a Rubik's Cube. The organization announced that the robot has learned to solve a Rubik's Cube one-handed, an accomplishment that speaks to the robot's dexterity in handling and manipulating the cube more than anything. Previously, we had seen the robot interact with unknown objects without any real-world training, only virtual simulations.

Robots with truly humanlike dexterity are far from becoming reality, but progress accelerated by AI has brought us closer to achieving this vision than ever before. In a research paper published in September, a team of scientists at Google detailed their tests with a robotic hand that enabled it to rotate Baoding balls with minimal training data. And at a computer vision conference in June, MIT researchers presented their work on an AI model capable of predicting the tactility of physical things from snippets of visual data alone. Now, OpenAI -- the San Francisco-based AI research firm cofounded by Elon Musk and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman -- says it's on the cusp of solving something of a grand challenge in robotics and AI systems: solving a Rubik's cube. Unlike breakthroughs achieved by teams at the University of California, Irvine and elsewhere, which leveraged machines tailor-built to manipulate Rubik's cubes with speed, the approach devised by OpenAI researchers uses a five-fingered humanoid hand guided by an AI model with 13,000 years of cumulative experience -- on the same order of magnitude as the 40,000 years used by OpenAI's Dota-playing bot.

Generalized planning is the task of generating a single solution that is valid for a set of planning problems. In this paper we show how to represent and compute generalized plans using procedural Domain Control Knowledge (DCK). We define a {\it divide and conquer} approach that first generates the procedural DCK solving a set of planning problems representative of certain subtasks and then compile it as callable procedures of the overall generalized planning problem. Our procedure calling mechanism allows nested and recursive procedure calls and is implemented in PDDL so that classical planners can compute and exploit procedural DCK. Experiments show that an off-the-shelf classical planner, using procedural DCK as callable procedures, can compute generalized plans in a wide range of domains including non-trivial ones, such as sorting variable-size lists or DFS traversal of binary trees with variable size.