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Open X-Embodiment: Robotic Learning Datasets and RT-X Models

arxiv.org · 11,294 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. 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 this possibility in the context of robotic manip

Open X-Embodiment: Robotic Learning Datasets and RT-X Models O pen X - E mbodiment Collaboration 0 0 {}^{0} start_FLOATSUPERSCRIPT 0 end_FLOATSUPERSCRIPT Abhishek Padalkar 7 7 {}^{7} start_FLOATSUPERSCRIPT 7 end_FLOATSUPERSCRIPT , Acorn Pooley 8 8 {}^{8} start_FLOATSUPERSCRIPT 8 end_FLOATSUPERSCRIPT , Ajay Mandlekar 15 15 {}^{15} start_FLOATSUPERSCRIPT 15 end_FLOATSUPERSCRIPT , Ajinkya Jain 11 11 {}^{11} start_FLOATSUPERSCRIPT 11 end_FLOATSUPERSCRIPT , Albert Tung 20 20 {}^{20} start_FLOATSUPERSCRIPT 20 end_FLOATSUPERSCRIPT , Alex Bewley 8 8 {}^{8} start_FLOATSUPERSCRIPT 8 end_FLOATSUPERSCRIPT

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