At Pantograph, we're working on training fully general robotics models that can act autonomously for hours at a time. Especially in robotics, it's difficult to get diverse data at scale. Learning to act from internet video data could allow models to scale with compute, rather than being limited by small action datasets. In this work, we develop a simple method for learning goal-directed behavior through pretraining on internet-scale video. Usually, goal-directedness is taught in a post-training phase, which limits the extent to which it can generalize. Here, we learn goal-directedness during pretraining on internet-scale video, which greatly improves the models' ability to achieve complex goals. Video games are a useful testing ground for robotics. We're starting with Minecraft because it's open-ended, and supports commanding diverse, long-horizon goals.…