Note that any GIF compression artifacts in this animation are not present in the dataset itself. After collecting a diverse dataset, we experimentally investigate how it can be used to enable general skill learning that transfers to new environments. First, we pre-train visual dynamics models on a subset of data from RoboNet, and then fine-tune them to work in an unseen test environment using a small amount of new data. The constructed test environments (one of which is visualized below) all include different lab settings, new cameras and viewpoints, held-out robots, and novel objects purchased after data collection concluded. Example test environment constructed in a new lab, with a temporary uncalibrated camera, and a new Baxter robot.