📌 Domain Randomisation Summary
Domain randomisation is a technique used in artificial intelligence, especially in robotics and computer vision, to make models more robust. It involves exposing a model to many different simulated environments where aspects like lighting, textures, and object positions are changed randomly. By training on these varied scenarios, the model learns to perform well even when faced with new or unexpected situations outside the simulation.
🙋🏻♂️ Explain Domain Randomisation Simply
Imagine practising football in different types of weather and on various pitches. By doing this, you become better at playing in any conditions, not just the perfect ones. Domain randomisation works in a similar way for AI, helping it learn to handle all sorts of real-life situations by training in lots of different, changing environments.
📅 How Can it be used?
Domain randomisation can be used to train a robot to recognise objects accurately in unpredictable real-world lighting and backgrounds.
🗺️ Real World Examples
A company developing a warehouse robot uses domain randomisation to train its vision system. They simulate countless warehouse scenes with different shelf arrangements, lighting, and box colours. This makes the robot reliable at spotting products in various real warehouses, even if they look quite different from the training simulations.
In autonomous driving research, developers use domain randomisation to vary weather, road textures, and vehicle models in simulations. This helps self-driving cars identify lanes, signs, and obstacles no matter the real-world conditions they encounter.
✅ FAQ
Why is domain randomisation important in training robots and AI systems?
Domain randomisation is important because it helps robots and AI systems handle surprises. By practising in lots of different, randomly mixed-up environments, these systems learn not to get confused when things change. This means they are less likely to make mistakes when they move from a virtual world to the real world, where things are never exactly the same as in training.
How does domain randomisation help AI models work better in real life?
When AI models are trained only on perfect or predictable data, they can struggle with anything unusual. Domain randomisation gets around this by mixing up things like lighting, backgrounds, and object positions during training. This makes the models more flexible and better at dealing with real-world messiness and surprises.
Can domain randomisation save time and resources in developing AI systems?
Yes, domain randomisation can save time and resources. By using computer simulations with lots of variety, developers can train AI systems without needing endless real-world tests. This approach helps spot problems early and means the AI is more likely to work well straight away when it faces real-life situations.
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