Open X-Embodiment

Open X-Embodiment (OXE) is a large-scale robot demonstration dataset assembled by Google DeepMind and 33 research institutions, comprising over 1 million robot episodes from 22 different robot embodiments and more than 527 skills. It was created to enable co-training across embodiments — the hypothesis being that diverse robot experience teaches richer manipulation representations than single-robot datasets alone. RT-X, the model trained on OXE, demonstrated positive transfer across embodiments and improved performance on held-out tasks compared to single-embodiment baselines. OXE data is publicly available and has catalyzed a wave of cross-embodiment robotics research.