Update README.md

README.md

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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 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.
+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.
 With 1M+ trajectories from 100 robots, AgiBot World spans 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.