Domain randomisation is a very popular method for visual sim-to-real transfer in robotics, due to its simplicity and ability to achieve transfer without any real-world images at all. But a number of design choices must be made to achieve optimal transfer. In this paper, we perform a large-scale benchmarking study on these  choices, with two key experiments evaluated on a real-world object pose estimation task , which is also a proxy for end-to-end visual control . First, we study the quality of the rendering pipeline , and find that a small number of high-quality images is superior to a large number of low-quality images. Second, we study the type of randomisation, and find that both distractors and textures are important for generalisation to novel environments.
Domain randomisation is a very popular method for visual sim-to-real transfer in robotics, due to its simplicity and ability to achieve transfer without any real-world images at all. Nonetheless, a number of design choices must be made to achieve optimal transfer. In this paper, we perform a comprehensive benchmarking study on these different choices, with two key experiments evaluated on a real-world object pose estimation task . First, we study the rendering quality , and find that a small number of high-quality images is superior to a large number of low-quality images. Second, we study the type of randomisation, and find that both distractors and textures are important for generalisation to novel environments.
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large-scale
comprehensive
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different
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, which is also a proxy for end-to-end visual control
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quality of the rendering pipeline
rendering quality