[R] Open X-Embodiment: Robotic Learning Datasets and RT-X Models - DeepMind 2023 - RT-X exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms!

Blog: [https://www.deepmind.com/blog/scaling-up-learning-across-many-different-robot-types](https://www.deepmind.com/blog/scaling-up-learning-across-many-different-robot-types)

[https://robotics-transformer-x.github.io/](https://robotics-transformer-x.github.io/) here you can also find the Datasets and Code!

Paper: [https://robotics-transformer-x.github.io/paper.pdf](https://robotics-transformer-x.github.io/paper.pdf)

Abstract:

>Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and …

abstract applications capacity computer computer vision consolidation datasets diverse domains general machinelearning nlp pretrained models robotic learning robotics vision