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Why Solving a Rubik’s Cube Does Not Signal Robot Supremacy

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Why Solving a Rubik’s Cube Does Not Signal Robot Supremacy


OpenAI’s system consists of a humanoid hand, from UK-based Shadow Robot Company, connected to a powerful computer system and an array of cameras and other sensors. Dactyl figures out how to manipulate something using reinforcement learning, which trains a neural network to control the hand based on extensive experimentation.

Reinforcement learning has produced other impressive AI demos. Most famously, DeepMind, an Alphabet subsidiary, used reinforcement learning to train a program called AlphaGo to play the devilishly difficult and subtle board game Go better than the best human players.

The technique has been used with robots as well. In 2008, Andrew Ng, an AI expert who would go on to hold prominent roles at Google and Baidu, used the technique to make drones perform aerobatics. A few years later, one of Ng’s students, Pieter Abbeel, showed that the approach can teach a robot to fold towels, although this never proved commercially viable. (Abbeel also previously worked part time at OpenAI and still serves as an adviser to the company).

Last year, OpenAI showed Dactyl simply rotating a cube in its hand using a motion learned through reinforcement learning. To wrangle the Rubik’s Cube, however, Dactyl didn’t rely entirely on reinforcement learning. It got help from a more conventional algorithm to determine how to solve the puzzle. What’s more, although Dactyl is equipped with several cameras, it cannot see every side of the cube. So it required a special cube equipped with sensors to understand how the squares are oriented.

Success in applying reinforcement learning to robotics have been hard won because the process is prone to failure. In the real world, it’s not practical for a robot to spend years practicing a task, so training is often done in simulation. But it’s often difficult to translate what works in simulation to more complex conditions, where the slightest bit of friction or noise in a robot’s joints can throw things off.

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This is where Dactyl’s real innovation comes in. The researchers devised a more effective way to simulate the complexity of the real world by adding noise, or perturbations to their simulation. In the latest work, this entails gradually adding noise so that the system gradually learns to be more robust to real-world complexity. In practice, it means the robot is able to learn, and transfer from simulation to reality, more complex tasks than previously demonstrated.

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Goldberg, the Berkeley professor, who was briefed on the work before it was released, says the simulated learning approach is clever and widely applicable. He plans to try using it himself, in fact.

But he says he would have liked to see the limits of the system presented more clearly. The failure rate, for example, was buried deep in the paper, and the video does not show the robot dropping the cube. “But they’re a company, and that’s the difference between academia and companies,” he adds.

Marcus sees Dactyl as the latest in a long line of attention-grabbing AI stunts. He points to a previous announcement from OpenAI, about an algorithm for generating text that was deemed “too dangerous to release,” as evidence the company is prone to oversell its work. “This is not the first time OpenAI has done this,” he says.

OpenAI did not respond to a request for comment.

The surest evidence of how far robots have to go before mastering human-like dexterity is the small range of repetitive tasks to which robots are limited in industry. Tesla, for example, has struggled to introduce more automation in its plants and Foxconn has been unable to have robots do much of the fiddly work involved with manufacturing iPhones and other gadgets.


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