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arXiv 2011.071122020-11cs.ROcs.CV

Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer

Imperial College London（伦敦帝国学院）

Raghad Alghonaim, Edward Johns

备注：: Published at ICRA 2021. For project page, please visit: https://www.robot-learning.uk/benchmarking-domain-randomisation

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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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