AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

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Grounded Action Transformation for Robot Learning in Simulation
Josiah P. Hanna, Peter Stone

Last modified: 2017-02-12


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. This paper proposes a new algorithm for learning in simulation — 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.


Grounded simulation learning; robot learning; simulation

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