@inproceedings{JosifovskiMalmirKlarmannetal.2022, author = {Josifovski, Josip and Malmir, Mohammadhossein and Klarmann, Noah and Žagar, Bare Luka and Navarro-Guerrero, Nicol{\´a}s and Knoll, Alois}, title = {Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks}, series = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan}, booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan}, doi = {10.1109/IROS47612.2022.9981951}, pages = {10193 -- 10200}, year = {2022}, abstract = {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}, language = {en} }