If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simulators of increasing fidelity (since reality is what it is). We challenge this assumption and present a contrary hypothesis -- sim2real transfer of robots may be improved with lower (not higher) fidelity simulation. We conduct a systematic large-scale evaluation of this hypothesis on the problem of visual navigation -- in the real world, and on 2 different simulators (Habitat and iGibson) using 3 different robots (A1, AlienGo, Spot). Our results show that, contrary to expectation, adding fidelity does not help with learning; performance is poor due to slow simulation speed (preventing large-scale learning) and overfitting to inaccuracies in simulation physics. Instead, building simple models of the robot motion using real-world data can improve learning and generalization.
翻译:如果我们想在实际部署机器人之前对机器人进行模拟培训,那么假定减少模拟现实差距意味着产生日益忠诚的模拟因素(因为现实就是现实)似乎是自然和几乎不言自明的。 我们质疑这一假设并提出了相反的假设 -- -- 模拟模拟(而不是更高)真实性模拟可以改善机器人的模拟转移。我们对这一假设进行系统的大规模评估,评估的是视觉导航问题 -- -- 在现实世界,以及使用3个不同机器人(A1, AlienGo, Spot)的2个不同的模拟器(Higend and iGibson)的模拟器(Higend and iGibson),我们的结果显示,与预期相反,增加忠诚无助于学习;由于模拟速度缓慢(防止大规模学习)和在模拟物理中过度适应不准确性,性表现不佳。相反,利用现实世界数据建立简单的机器人运动模型可以改善学习和普及。