README.md

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title: README
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Most existing robot learning benchmarks struggle to address real-world challenges caused by low-quality data and limited sensing capabilities, typically limited to short-horizon tasks within controlled environments. Introducing AgiBot World, the first large-scale robotic learning dataset designed to advance multi-purpose humanoid robotic policies. It is to be accompanied by foundation models, benchmarks, and an ecosystem to democratize access to high-quality robot data for the academic community and the industry, paving the path towards the "ImageNet Moment" for Embodied AI. The AgiBot World Beta dataset contains 1M+ trajectories, with a total duration of 2,976.4 hours, covering 217 specific tasks, 87 skills, 3,000+ different objects, and 100+ real-world scenarios across five target domains, tackling fine-grained manipulation, tool usage, and multi-robot collaboration. Cutting-edge multimodal hardware features visual tactile sensors, durable 6-DoF dexterous hands, and mobile dual-arm robots with whole-body control, supporting research in imitation learning, multi-agent collaboration, and more. AgiBot World aspires to transform large-scale robot learning and advance scalable robotic systems for production. This open-source platform invites researchers and practitioners to collaboratively shape the future of Embodied AI.
The AgiBot World Alpha dataset is a subset of the Beta dataset, maintaining consistency in content and aligning with the Beta dataset's standards, ensuring that users working with the Alpha dataset have access to a representative sample of the broader Beta dataset. For a detailed comparison between the Alpha and Beta datasets, please refer to [docs.google.com](https://docs.google.com/spreadsheets/d/1GWMFHYo3UJADS7kkScoJ5ObbQfAFasPuaeC7TJUr1Cc/edit?gid=429909968#gid=429909968)