论文标题
通过模拟器调整遍历现实差距
Traversing the Reality Gap via Simulator Tuning
论文作者
论文摘要
对模拟数据的巨大需求使现实差距成为机器人技术最前沿的问题。我们提出了一种通过调整可用的仿真参数来遍历间隙的方法。通过优化物理引擎参数，我们表明我们能够缩小模拟解决方案和现实世界数据集之间的差距，从而可以在两者之间进行更多的倾斜行为转移。随后，我们了解了特定模拟器参数的重要性，这对机器人机器学习社区具有广泛的兴趣。我们发现，即使针对不同的任务进行了优化，即不同的物理发动机在某些情况下的性能更好，而摩擦和最大执行速度是紧密界限的参数，极大地影响了模拟解决方案的转移。
The large demand for simulated data has made the reality gap a problem on the forefront of robotics. We propose a method to traverse the gap by tuning available simulation parameters. Through the optimisation of physics engine parameters, we show that we are able to narrow the gap between simulated solutions and a real world dataset, and thus allow more ready transfer of leaned behaviours between the two. We subsequently gain understanding as to the importance of specific simulator parameters, which is of broad interest to the robotic machine learning community. We find that even optimised for different tasks that different physics engine perform better in certain scenarios and that friction and maximum actuator velocity are tightly bounded parameters that greatly impact the transference of simulated solutions.
