论文标题
在模拟中，机器人学习的随机接地动作转化
Stochastic Grounded Action Transformation for Robot Learning in Simulation
论文作者
论文摘要
在模拟中学习的机器人控制政策通常不会很好地转移到现实世界中。许多现有的对SIM到现实问题的解决方案，例如接地的动作转换（GAT）算法，试图通过将模拟器与现实世界匹配来纠正或扎根这些差异。但是，如果这些方法不明确说明目标环境中的随机性，则这些方法的疗效将受到限制。在这项工作中，我们分析了与在随机世界环境中接地确定性模拟器相关的问题，并提出了示例，其中GAT由于目标域中的随机过渡而无法转移良好的政策。作为回应，我们介绍了随机接地的动作转化（SGAT）算法，该算法在接地模拟器时建模了这种随机性。我们在实验中发现了SGAT可以找到在目标域中具有鲁棒性的策略的模拟和物理目标域的实验。
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
