RT-1 (Robotics Transformer 1) is the foundational predecessor to RT-2 and marks one of the first successful applications of large-scale transformer models to real-world robot control. Developed by a large team at Google DeepMind, Google Research, and Everyday Robots, RT-1 is trained on a dataset of over 130,000 real-world episodes collected across 13 months using a fleet of mobile manipulators in office kitchen environments. It demonstrated that high-capacity sequence models, when fed with enough diverse robotic data, could generalize across tasks, robots, and environmental settings far better than previous specialist policies. The model architecture is a decoder-only transformer that ingests a history of RGB images and a natural language instruction, then predicts discretized motor commands for a 7-DOF arm, a base, and a gripper. Importantly, RT-1 does not use privileged state information; it learns entirely from pixels and text. Action discretization converts continuous joint positions into tokens, allowing the model to be trained with standard next-token cross-entropy loss. This unification of vision, language, and action within a single autoregressive framework proved remarkably effective, achieving over 97% success rate on a broad set of manipulation skills in previously unseen environments. One of the most significant contributions of RT-1 is its evidence for the scaling law of robotics data. By training on orders of magnitude more real robot interactions than prior work, the authors observe clear performance improvements that mirror the trends seen in language and vision models. The model also shows positive transfer when trained on datasets from multiple robots, suggesting that a shared representation of manipulation is learnable across embodiments. For the research community, this paper validated the hypothesis that robotic foundation models could follow the same data-scaling playbook as NLP and vision. RT-1 also serves as the architectural backbone for the Open X-Embodiment dataset and Octo. Many subsequent papers cite RT-1 as proof that a unified action-generation model, rather than a modular perception-planning-control stack, can solve complex long-horizon tasks when grounded in large-scale data. Its success directly paved the way for RT-2 by showing that the transformer paradigm could accommodate both visual and action modalities without specialized robotics-specific inductive biases beyond tokenization.