![Four photos show, on top level, a simulation of a robot hand using a spatula, knife, hammer and wrench. The second row shows a real robot hand performing the tasks, and the bottom row shows a human hand performing the tasks.](https://news.mit.edu/sites/default/files/styles/news_article__image_gallery/public/images/202405/MIT-Policy-Comp-01-press.jpg?itok=RpPn-Bwu)
Credits:Image: Courtesy of the researchers
With generative AI models, researchers combined robotics data from different sources to help robots learn better. MIT researchers developed a technique to combine robotics training data across domains, modalities, and tasks using generative AI models. They create a combined strategy from several different datasets that enables a robot to learn to perform new tasks in unseen environments.
Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver...
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