We have always longed for robots that are as smart as humans, but training robots is far more difficult than imagined. Traditional training methods are either expensive or ineffective. To solve this problem, researchers at Stanford University have come up with an ingenious solution - digital cousins. The editor of Downcodes will give you an in-depth understanding of this breakthrough technology, how it reduces training costs, enhances the robustness of robots, and ultimately drives robotics technology to new heights.
We have always dreamed of having robots that are as smart as humans, that can help us with housework, chat with us, and even be as omnipotent as Jarvis in Iron Man. However, the ideal is very full and the reality is very skinny. Teaching a robot to do things cannot be done by just patting on the head. It is as difficult as teaching a girlfriend to reason, and it may not be effective even if it takes a lot of effort.
Why? Because the real world is too complex and full of accidents and changes. Think about it, you have to spend a lot of time to teach your girlfriend a simple truth, let alone teach a robot without human thinking?
Traditional robot training methods are either too costly , require repeated trials in the real world, and may cause safety hazards; or they are too ineffective , and robots trained in simulated environments will become blind as soon as they arrive in the real world, like a mentally retarded child. Similar.
To solve this problem, researchers at Stanford University came up with a genius idea: digital cousins .
What are digital cousins?
Simply put, digital cousins are virtual stand-ins for real-world objects . You can think of it as a high imitation digital model. It looks similar to the real object and has similar functions, but it does not need to be exactly the same .
For example, a real-world cabinet and its digital cousin should have similar handles and drawer layouts, but the materials and details could be different. Likewise, a real-world kitchen and its digital cousin should have similar furniture placement, but the specific model can differ slightly.
Why get this digital cousin? Because it has two huge advantages:
Reduced costs: Digital cousins don’t need to replicate the real world as precisely as digital twins, so they are simpler and cheaper to make.
Enhanced robustness: A real-life object can have multiple digital cousins, and these cousins can have subtle differences. This is equivalent to providing the robot with more diverse training data so that it can learn to deal with various changes.
How to automatically generate digital cousins?
Researchers at Stanford University have developed a system called ACDC that can automatically generate digital cousin scenes from a single RGB image . This system is great news for lazy people. You only need to take a photo, and it will help you generate a virtual training ground so that your robot can play in it.
The workflow of the ACDC system is roughly divided into three steps:
Extract information: Extract the object's mask, depth information, etc. from the input RGB image.
Matching Cousins: Based on the extracted information, find the digital model from the database that is most similar to the real-life object, and adjust the size and orientation of the model according to the object category and characteristics.
Generate scenes: Combine matched digital models to generate a complete virtual scene, and make physical adjustments to ensure the stability and rationality of the scene.
Do digital cousins really work?
Researchers at Stanford University conducted a series of experiments that showed that robots trained with digital cousins performed better:
Simulated environment: In a simulated environment, robots trained with digital cousins have a higher success rate when completing tasks such as opening doors, opening drawers, and placing bowls, and are more adaptable to different models of furniture . In contrast, robots trained with digital twins tend to act foolishly once they encounter furniture they have never seen before.
Real world: In the real world, robots trained with digital cousins can be directly applied to real-world scenarios without additional fine-tuning . Robots trained with digital twins require additional adjustments to adapt to real-world differences.
The emergence of digital cousin technology has opened a new door for robot learning. The robots of the future will be smarter, more flexible, and better able to adapt to the complex and ever-changing real world.
Of course, this technology currently still has some limitations. For example, the number and types of models in the database are not rich enough, and the handling of some special situations is not perfect enough. But with the advancement of technology and the accumulation of data, these problems will gradually be solved.
All in all, digital cousin technology has a bright future and will push robotics to the next level. In the near future, we may actually be able to have robot companions as smart as humans.
Project address: https://digital-cousins.github.io/
Paper address: https://arxiv.org/pdf/2410.07408
The "digital cousin" technology proposed by Stanford University provides a new way of thinking for robot training, reduces costs, improves efficiency, and lays a solid foundation for more intelligent and flexible robots in the future. I believe that with the continuous development of technology, "digital cousins" will promote greater breakthroughs in robotics technology and ultimately realize our dream of harmonious coexistence with intelligent robots.