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[1910.07113] Solving Rubik's Cube with a Robot Hand

arxiv.org · 7,730 words · saved by 1 readers

Abstract:We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: this https URL

S OLVING RUBIK ’ S C UBE WITH A ROBOT H AND A P REPRINT OpenAI Ilge Akkaya∗, Marcin Andrychowicz∗, Maciek Chociej∗, Mateusz Litwin∗, Bob McGrew∗, Arthur Petron∗, Alex Paino∗, Matthias Plappert∗, Glenn Powell∗, Raphael Ribas∗, Jonas Schneider∗, Nikolas Tezak∗,…

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