Solving a Rubik’s Cube is hard enough, but a human-like robot hand recently completed the task with some high-tech help.
For the past two years, OpenAI, a company based in San Francisco, has been trying to train the robot hand to solve a Rubik’s Cube, said a press release. Researchers managed to solve it via simulation in July 2017 and in July 2018, they were only able to manipulate a block on the robot hand. Now, the company says it has reached its goal: better dexterity, which has helped the robot hand unscramble the Rubik’s Cube.
OpenAI made the magic happen with a pair of neural networks: Researchers trained them to solve the Rubik’s Cube in simulation with Kociemba’s algorithm, an optimal solution for the Rubik’s Cube, and reinforcement learning. Domain randomization then allows neural networks trained in simulation to transfer face rotations and cube flips to the robot hand.
Testing didn’t end there though—factors like dynamics, friction, and elasticity could make movements for the robot quite difficult. To work through this challenge, researchers created Automatic Domain Randomization (ADR), a new method that generates difficult environments in a simulated setting.
With ADR, researchers were able to train neural networks in simulation that can unscramble the Rubik’s Cube on the robot hand. ADR exposes the network to more randomized situations during the training phase.
Researchers applied perturbations to the robot hand while it solved the Rubik’s Cube. (Photo Credit: OpenAI)
Researchers also added perturbations, including a plush giraffe and tied fingers, to see if it would alter the robot hand’s performance. The team discovered that the robot hand could successfully engage in most face rotations and flips under the tested perturbations except at peak performance.
The robot hand hasn’t reached Rubik’s Cube master status yet, but the testing above is a great start for human-level dexterity.
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