Open X-Embodiment represents an unprecedented community-scale effort to consolidate the fragmented landscape of robot learning datasets. The collaboration spans 21 institutions and 22 distinct robotic platforms—from single-arm tabletop manipulators to mobile manipulators and humanoids—resulting in a unified dataset of over 160,000 real-world manipulation tasks demonstrating 527 skills. Each trajectory was meticulously standardized into a common data format with consistent action spaces, observation modalities, and metadata schemas, making it feasible to train a single policy across hardware that was previously considered incompatible. This cross-embodiment dataset naturally raises the question: can a single model learn from such heterogeneous data and transfer knowledge across platforms? The authors provide a definitive affirmative answer in the form of RT-X, a high-capacity transformer-based policy trained end-to-end on the full Open X-Embodiment dataset. RT-X demonstrates strong positive transfer: robots trained with the generalist model outperform their specialist counterparts when evaluated on held-out tasks. A Franka robot, for instance, achieves higher success rates after being initialized with RT-X than after training only on Franka-specific data. This suggests that the generalist model extracts shared physical priors—such as how to grasp, push, or assemble—that transcend individual morphologies. The dataset is released alongside a set of baseline models and rigorous evaluation protocols. The authors systematically investigate which training strategies work best for multi-robot pre-training, finding that action tokenization and cross-embodiment batching are critical for stable optimization. They also show that data diversity—spanning different grippers, cameras, and environments—is more important than raw quantity for generalization. The standardized data format and open release are expected to serve as the bedrock for the next generation of generalist robot policies, much as ImageNet did for computer vision and the Pile did for language modeling. Long-term, Open X-Embodiment aims to catalyze the same consolidation seen in NLP and computer vision: from bespoke per-robot solutions to unified foundation models that can be fine-tuned with modest data for any new platform. The project website provides dataset viewers, download tools, and integration guides, dramatically lowering the entry barrier for labs that wish to contribute to or leverage this shared resource.