World Knowledge from AI Image Generation for Robot Control

Real images encode a lot of information about the world, such as how an object can look like, how certain things can be meaningfully arranged, or which items belong together. The image of an average office desk can give us information about how the different parts are usually arranged in relation to each other, e.g. a monitor on the desk with mouse and keyboard in front of it and a chair in front of the desk, or the image of someone preparing a meal can give us information about which ingredients and kitchen tools are to be used. This might seem rather trivial from a human perspective as we are very easily capable of handling such tasks without having to rely on pre-made example images to follow, but for a robot that has to navigate and solve tasks in e.g. a household environment such information can be critical for successfully handling everyday-activities and interacting with the world. We could encode all relevant information explicitly into an extensive knowledge base [1] for the robot, but considering the number of tasks and circumstances that a robot could encounter, correctly handling all situations could become very challenging [2] or even overwhelming when the robot needs to act in widely different environments. Additional knowledge sources, such as simulations of the environment, when available, can help by providing ways to investigate consequences of actions without having to act in the world [3]. We could also try to train the robot on a variety of different tasks, e.g. using reinforcement learning or other methods [4], hoping that the robot is able to generalize and handle situations and circumstances that were never seen during training. However, images of the real world already show examples of how a dining table looks like with plates and cutlery, how images are hung on the wall in bedrooms, dining rooms, or other places. Figure 1 shows an example of two different versions of how sandwich ingredients could be stacked together.