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.

Our goal is to pre-train reinforcement learning models on a diverse dataset and then transfer knowledge (either zero-shot or with fine-tuning) to a different test environment. In the last decade, we've seen learning-based systems provide transformative solutions for a wide range of perception and reasoning problems, from recognizing objects in images to recognizing and translating human speech. If fruitful, this line of work could allow learning-based systems to tackle active control tasks, such as robotics and autonomous driving, alongside the passive perception tasks to which they have already been successfully applied. While deep reinforcement learning methods – like Soft Actor Critic– can learn impressive motor skills, they are challenging to train on large and broad data that is not from the target environment. In contrast, the success of deep networks in fields like computer vision was arguably predicated just as much on large datasets, such as ImageNet, as on large neural network architectures.