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The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to generate such data sets on a physical platform. Therefore, state-of-the-art approaches learn in simulation where data generation is fast as well as inexpensive and subsequently transfer the knowledge to the real robot (sim-to-real). Despite becoming increasingly realistic, all simulators are by construction based ondoi:10.3389/frobt.2022.799893 pmid:35494543 pmcid:PMC9038844 fatcat:f7bytfvmgjfnllmnuy74ywnxau