We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.
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 modelling of the world. more…

https://openai.com/blog/learning-dexterity/