It’s tricky to get robots to do things in environments they’ve never seen before. Typically, researchers need to train them on new data for every new place they encounter, which can become very time-consuming and expensive. Now researchers have developed a series of AI models that teach robots to complete basic tasks in new surroundings without further training or fine-tuning. The five AI models, called robot utility models (RUMs), allow machines to complete five separate tasks—opening doors and drawers, and picking up tissues, bags, and cylindrical objects—in unfamiliar environments with a 90% success rate. The team, consisting of researchers from New York University, Meta, and the robotics company Hello Robot, hopes its findings will make it quicker and easier to teach robots new skills while helping them function within previously unseen domains. The approach could make it easier and cheaper to deploy robots in our homes. “In the past, people have focused a lot on the problem of ‘How do we get robots to do everything?’ but not really asking ‘How do we get robots to do the things that they do know how to do—everywhere?’” says Mahi Shafiullah, a PhD student at New York University who worked on the project. “We looked at ‘How do you teach a robot to, say, open any door, anywhere?’” Teaching robots new skills generally requires a lot of data, which is pretty hard to come by. Because robotic training data needs to be collected physically—a time-consuming and expensive undertaking—it’s much harder to build and scale training databases for robots than it is for types of AI like large language models, which are trained on information scraped from the internet.
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