# Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer

arXiv: https://arxiv.org/abs/2011.07112 · HTML: https://mlsanity.com/paper/2011.07112/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [Robot Learning from Randomized Simulations: A Review](https://mlsanity.com/paper/2111.00956/) (2021)
- [SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo](https://mlsanity.com/paper/2106.16118/) (2021)
- [Green Screen Augmentation Enables Scene Generalisation in Robotic Manipulation](https://mlsanity.com/paper/2407.07868/) (2024)
- [Sim2Real Instance-Level Style Transfer for 6D Pose Estimation](https://mlsanity.com/paper/2203.02069/) (2022)
- [Coarse-to-Fine for Sim-to-Real: Sub-Millimetre Precision Across Wide Task Spaces](https://mlsanity.com/paper/2105.11283/) (2021)
- [Learning Eye-in-Hand Camera Calibration from a Single Image](https://mlsanity.com/paper/2111.01245/) (2021)
- [ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation](https://mlsanity.com/paper/2403.16400/) (2024)
- [Photo-realistic Neural Domain Randomization](https://mlsanity.com/paper/2210.12682/) (2022)
- [Robust Visual Sim-to-Real Transfer for Robotic Manipulation](https://mlsanity.com/paper/2307.15320/) (2023)
- [Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation](https://mlsanity.com/paper/2403.11511/) (2024)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning](https://mlsanity.com/paper/2301.12249/) (2023)
- [Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data](https://mlsanity.com/paper/2411.09077/) (2024)
- [Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection](https://mlsanity.com/paper/2403.04809/) (2024)
- [InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies](https://mlsanity.com/paper/2609.27734/) (2026)

## Abstract (arXiv)

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.