確率的接地行動変換によるシミュレーション内ロボット学習
Stochastic Grounded Action Transformation for Robot Learning in Simulation
実環境の確率的な遷移を考慮してシミュレータを接地するSGATを提案し、シミュレーションと実機の両方で確率的な環境に頑健な方策を得られることを示した。
著者: Siddharth Desai, Haresh Karnan, Josiah P. Hanna, Garrett Warnell, Peter Stone
分類: cs.RO
原文アブストラクト
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
