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We demonstrate that models trained only in simulation can be used to solve amanipulation problem of unprecedented complexity on a real robot. This is madepossible by two key components: a novel algorithm, which we call automaticdomain randomization (ADR) and a robot platform built for machine learning. ADRautomatically generates a distribution over randomized environments ofever-increasing difficulty. Control policies and vision state estimatorstrained with ADR exhibit vastly improved sim2real transfer. For controlpolicies, memory-augmented models trained on an ADR-generated distribution ofenvironments show clear signs of emergent meta-learning at test time. Thecombination of ADR with our custom robot platform allows us to solve a Rubik'scube with a humanoid robot hand, which involves both control and stateestimation problems. Videos summarizing our results are available:https://openai.com/blog/solving-rubiks-cube/

Solving Rubik's Cube with a Robot Hand