Inside the robotics laboratory of the Toyota Research Institute (TRI) in Cambridge, Massachusetts, a group of robots are busy cooking. There is nothing special about that; robotic chefs have been around for a while. But these robots are more proficient than most, flipping pancakes, slicing vegetables and making pizzas with ease. The difference is that instead of being laboriously programmed to carry out their tasks, the Cambridge robots have been taught only a basic set of skills. Using the wonders of artificial intelligence (AI), they quickly improved upon those skills to become far more dexterous. Despite their extraordinary culinary capabilities, these robots are not destined for a career in catering. “If you give a robot the confidence to work in a kitchen, it will also have the confidence to work in a factory or a person’s home,” says Gill Pratt, Toyota’s chief scientist. Cooking involves lots of complex tasks, such as picking up and placing items, pouring liquids and mixing ingredients. All this makes a kitchen an ideal training ground for experimenting with a new method of using generative AI to train robots known as “diffusion policy”. Diffusion, already used to help AI models generate images, has been developed as a way to speed up the training of robots by TRI and roboticists at Columbia University and the Massachusetts Institute of Technology (MIT). To explain how diffusion works, Russ Tedrake, TRI’s vice-president of robotics research and a professor at MIT, uses a typical kitchen task: teaching a robot how to load a dishwasher, once its fellow machines are done with their cooking.

Full commentary : Robots can learn new actions faster thanks to AI techniques.