Robot control policies learned in simulation do not often transfer well to
the real world. Many existing solutions to this sim-to-real problem, such as
the Grounded Action Transformation (GAT) algorithm, seek to correct for or
ground these differences by matching the simulator to the real world. However,
the efficacy of these approaches is limited if they do not explicitly account
for stochasticity in the target environment. In this work, we analyze the
problems associated with grounding a deterministic simulator in a stochastic
real world environment, and we present examples where GAT fails to transfer a
good policy due to stochastic transitions in the target domain. In response, we
introduce the Stochastic Grounded Action Transformation(SGAT) algorithm,which
models this stochasticity when grounding the simulator. We find experimentally
for both simulated and physical target domains that SGAT can find policies that
are robust to stochasticity in the target domain

分析了用 Grounded Action Transformation 算法来解决 sim-to-real 问题的局限性，因为它们没有明确考虑到目标环境中的随机性，提出了 Stochastic Grounded Action Transformation 算法，这种算法在基于模拟的控制器在真实环境中无法成功迁移时有很好的应用。