AI video tech fast-tracks humanoid robot training

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Vidar works by decoupling perception from control. First, it uses ShengShu's Vidu video model to learn from both real and synthetic videos. Then, a task-agnostic system called AnyPos translates that knowledge into motor commands for robots. This modular setup allows for faster training and easier deployment across different types of robots.

Unlike traditional methods that require robots to physically interact with the world to learn, Vidar can simulate complex, lifelike scenarios virtually. Remarkably, it only needs about 20 minutes of training data, between 1/80 and 1/1200 of what leading models require. That efficiency makes it possible to scale robot training to levels never seen before.

Vidar is more than just a research tool. Its design means robots can adapt quickly to new tasks and environments. That could unlock real-world applications in eldercare, home assistance, healthcare and smart manufacturing. By bridging the gap between simulation and reality, Vidar is positioning humanoid robots as practical helpers rather than futuristic concepts.

For consumers, Vidar brings the idea of household or workplace robot helpers closer to reality. Instead of waiting decades for robots to mature, scalable training could speed up deployment in everyday settings. This could mean robots assisting you with chores, supporting eldercare or even helping in medical environments sooner than expected.

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Vidar is a milestone in the race toward practical humanoid robots. By blending limited real data with generative video, ShengShu has created a smarter and faster way to train physical AI. The approach tackles cost, efficiency and scalability all at once, three factors that have long held robotics back.

Would you welcome a humanoid robot in your home if it could help with daily tasks, or does the idea still feel too futuristic? Let us know by writing to us at Cyberguy.com.

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