OpenAI has trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Their system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics.
Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. The 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. The Researchers 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 challenge in robotic control. Unlike other problems such as locomotion, progress on dextrous manipulation using traditional robotics approaches has been slow, and current techniques remain limited in their ability to manipulate objects in the real world.
Reorienting an object in the hand requires many problems to be solved. Dactyl learns to solve the object reorientation task entirely in simulation without any human input. After this training phase, the learned policy works on the real robot without any fine-tuning.
Learning methods for robotic manipulation face a dilemma. Simulated robots can easily provide enough data to train complex policies, but most manipulation problems can’t be modeled accurately enough for those policies to transfer to real robots. Training directly on physical robots allows the policy to learn from real-world physics, but today’s algorithms would require years of experience to solve a problem like object reorientation.
OpenAI’s approach “domain randomization” learns in a simulation which is designed to provide a variety of experiences rather than maximizing realism. This gives the best of both approaches.
News Source: https://blog.openai.com/learning-dexterity/
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