@inproceedings{Hanna_Grounded_2017,
	title={{Grounded} {Action} {Transformation} {for} {Robot} {Learning} {in} {Simulation}},
	author={Hanna, Josiah and Stone, Peter},
	year={2017},
	month={February},
	booktitle={Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI)},
	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.
	},
}