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Learning dexterity | OpenAI

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Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we’ve been working on for the past⁠ year⁠. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five⁠. Our results⁠ show that it’s possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world. Dactyl is a system for manipulating objects using a Shadow Dexterous Hand⁠ (opens in a new window) . We place an object such as a block or a prism in the palm of the hand and ask Dactyl to reposition it into a different orientation; for example, rotating the block to put a new face on top. The network observes only the coordinates of the fingertips and the images from three regular RGB cameras. Although the first humanoid hands were developed decades ago, using them to manipulate objects effectively has been a long-standing chall

July 30, 2018 Milestone Learning dexterity We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Read paper (opens in a new window) Illustration: Ben Barry & Eric Haines Loading… Share Our system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics using techniques we’ve been working on for the past ⁠ year ⁠ . Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five ⁠ . Our results ⁠ show that it’s possible to train agents in

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