Co-training

Co-training in robotics refers to training a single policy on data from multiple robot embodiments, tasks, or environments simultaneously. The hypothesis is that diverse data sources teach the policy robust visual and behavioral representations that transfer better to new settings. The Open X-Embodiment dataset was assembled specifically to enable co-training across more than 22 robot types. Large foundation models like RT-2 and OpenVLA rely on co-training with internet-scale vision-language data alongside robot demonstration data to bootstrap generalization.