The new model, called RFM-1, was trained on years of data collected from Covariant's small fleet of item-picking robots that customers like Crate & Barrel and Bonprix use in warehouses around the world, as well as words and videos from the internet. In the coming months, the model will be released to Covariant customers. The company hopes the system will become more capable and efficient as it's deployed in the real world. In a demonstration I attended last week, Covariant cofounders Peter Chen and Pieter Abbeel showed me how users can prompt the model using five different types of input: text, images, video, robot instructions, and measurements. For example, show it an image of a bin filled with sports equipment, and tell it to pick up the pack of tennis balls. The robot can then grab the item, generate an image of what the bin will look like after the tennis balls are gone, or create a video showing a bird's-eye view of how the robot will look doing the task.

They have already revolutionized the way we live and work, and they still have the potential to do it again. They changed the way we live by doing mundane tasks for us, like vacuuming. Moreover, and more importantly, they changed the way we produce. Robots can perform complex tasks with speed, precision, and efficiency that far exceeds what humans are capable of. Robots helped us to significantly increase productivity and output in industries such as manufacturing, logistics, and agriculture.