Robots can learn new actions faster thanks to AI techniques

Inside the robotics laboratory of the Toyota Research Institute in Cambridge, Massachusetts, a group of robots are busy cooking. 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. For robot training, the AI uses the actions it has been taught to randomly generate potential new movements, which are then refined into useful actions that can deal with new environments. Widely seen as one of the world’s leaders in developing walking robots, Boston Dynamics is working on a lighter and smaller version of Atlas, its hulking humanoid, which can run, jump and even perform cartwheels.