Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this problem, we define an easy-to-reproduce experimental setup for a robotic reach-and-balance manipulator task, which can serve as a benchmark for comparison. We compare four randomization strategies with three randomized parameters both in simulation and on a real robot. Our results show that more randomization helps in Sim2Real transfer, yet it can also harm the ability of the algorithm to find a good policy in simulation. Fully randomized simulations and fine-tuning show differentiated results and translate better to the real robot than the other approaches tested.

翻译：随机化目前是机器人中数据驱动学习算法的Sim2Real传输中广泛使用的一种方法。 然而,大多数Sim2Real研究都报告特定随机化技术的结果,而且往往报告高度定制的机器人系统的结果,因此很难系统地评估不同的随机化方法。为了解决这一问题,我们定义了一种易于生成的机器人伸缩和平衡操纵器任务实验设置,这可以作为比较的基准。我们比较了四种随机化战略,在模拟和真实机器人中有三个随机化参数。我们的结果显示,更随机化有助于Sim2Real的传输,但也会损害算法在模拟中找到好政策的能力。完全随机化的模拟和微调显示有区别的结果,并将比其他测试的方法更好地转化为真正的机器人。