Large policies, pretrained on diverse robot datasets have the potential to transform robot learning, instead of training new policies from scratch, such generalist robot policies may be finetuned with only a little in-domain data, yet generalize broadly. However, existing models restrict downstream users to the exact inputs and action spaces used during pretraining and the largest models are typically not available to the public. In this work, we aim to lay the ground work towards developing open-source, widely applicable, generalist policies for robotic manipulation. As a first step, we introduce Octo, a transformer-based diffusion policy trained on 700K robot trajectories from the Open X-Embodiment dataset. It can be instructed via language commands or goal images and can be effectively finetuned to robot setups with new sensory inputs and action spaces within a few hours on standard consumer GPUs. In experiments across 6 robotic platforms we demonstrate that Octo serves as a versatile policy initialization that can be effectively finetuned to new observation and action spaces.