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Randomization is currently a widely used approach in Sim2Real transfer fordata-driven learning algorithms in robotics. Still, most Sim2Real studiesreport results for a specific randomization technique and often on a highlycustomized robotic system, making it difficult to evaluate differentrandomization approaches systematically. To address this problem, we define aneasy-to-reproduce experimental setup for a robotic reach-and-balancemanipulator task, which can serve as a benchmark for comparison. We comparefour randomization strategies with three randomized parameters both insimulation and on a real robot. Our results show that more randomization helpsin Sim2Real transfer, yet it can also harm the ability of the algorithm to finda good policy in simulation. Fully randomized simulations and fine-tuning showdifferentiated results and translate better to the real robot than the otherapproaches tested.

Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks