Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0
University of California, Berkeley · University of Edinburgh · +32 more institutions
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 even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore…
Citation impact
- FWCI
- 34.93
- Percentile
- 100%
- References
- 160
Authors
279Topics & keywords
- Computer science
- Computer graphics (images)
- Artificial intelligence
- Human–computer interaction