Grounded Action Transformation for Robot Learning in Simulation

Authors

  • Josiah Hanna The University of Texas at Austin
  • Peter Stone The University of Texas at Austin

DOI:

https://doi.org/10.1609/aaai.v31i1.11124

Keywords:

Grounded simulation learning, robot learning, simulation

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. 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.

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Published

2017-02-12

How to Cite

Hanna, J., & Stone, P. (2017). Grounded Action Transformation for Robot Learning in Simulation. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11124