%0 Journal Article %1 OpenAI2019SolvingRC %A OpenAI, %A Akkaya, Ilge %A Andrychowicz, Marcin %A Chociej, Maciek %A Litwin, Mateusz %A McGrew, Bob %A Petron, Arthur %A Paino, A. %A Plappert, Matthias %A Powell, Glenn %A Ribas, Raphael %A Schneider, Jonas %A Tezak, N. %A Tworek, Jerry %A Welinder, P. %A Weng, Lilian %A Yuan, Qiming %A Zaremba, Wojciech %A Zhang, Lei M. %D 2019 %J ArXiv %K robotics machine-learning rubiks-cube posted_with_chatgpt %T Solving Rubik's Cube with a Robot Hand

@article{OpenAI2019SolvingRC, added-at = {2023-07-26T09:55:34.000+0200}, author = {OpenAI and Akkaya, Ilge and Andrychowicz, Marcin and Chociej, Maciek and Litwin, Mateusz and McGrew, Bob and Petron, Arthur and Paino, A. and Plappert, Matthias and Powell, Glenn and Ribas, Raphael and Schneider, Jonas and Tezak, N. and Tworek, Jerry and Welinder, P. and Weng, Lilian and Yuan, Qiming and Zaremba, Wojciech and Zhang, Lei M.}, biburl = {https://www.bibsonomy.org/bibtex/269f9b9d7460eaada9d06e6a1b31428d5/dblp_test}, description = {This paper demonstrates 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.}, interhash = {6002b871dbb25c4fe833fe8fc10836f4}, intrahash = {69f9b9d7460eaada9d06e6a1b31428d5}, journal = {ArXiv}, keywords = {robotics machine-learning rubiks-cube posted_with_chatgpt}, timestamp = {2024-04-29T15:49:15.000+0200}, title = {Solving Rubik's Cube with a Robot Hand}, year = 2019 }