Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
| Autor: | Josifovski, Josip, Malmir, Mohammadhossein, Klarmann, Noah, Žagar, Bare Luka, Navarro-Guerrero, Nicolás, Knoll, Alois |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 |
| Databáze: | arXiv |
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