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@InProceedings{AAAI17-Hanna,
  author = {Josiah Hanna and Peter Stone},
  title = {Grounded Action Transformation for Robot Learning in Simulation},
  booktitle = {Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI)},
  location = {San Francisco, CA},
  month = {February},
  year = {2017},
  abstract = {
Robot learning in simulation is a promising alternative to the prohibitive sample cost of learning in the physical world. Unfortunately, policies learned in simulation often perform worse than hand-coded policies when applied on the physical robot. Grounded simulation learning (GSL) promises to address this issue by altering the simulator to better match the real world.  This paper proposes a new algorithm for GSL -- Grounded Action Transformation -- and applies it to learning of humanoid bipedal locomotion.  Our approach results in a 43.27\% improvement in forward walk velocity compared to a state-of-the art hand-coded walk. We further evaluate our methodology in controlled experiments using a second, higher-fidelity simulator in place of the real world. Our results contribute to a deeper understanding of grounded simulation learning and demonstrate its effectiveness for learning robot control policies. 
  },
}