机器人领域正在发生一件大事——而且它就藏在我们眼前。 这篇内容并不是关于会跳舞的机器人，而是关于驱动它们的数据。今年，开放式机器人数据集迎来爆发式增长，使整个领域转变为一个更具规模化、协作性的生态系统。 短短两年内， Hugging Face 上的数据集数量从 1.1 万增长到 60 多万——而其中机器人数据集是增长最快的板块。我们在 2024 年只有 1,000 个机器人数据集，而到 2025 年已增长至 27,000 个！ 作为对比，第二大类别“文本生成”在 2025 年也只有约 5,000 个数据集。这个差距非常巨大。 开放数据集之所以重要，是因为机器人领域的成败完全取决于真实世界的机器人数据——视频、动作、传感器、失败案例等。通过让这些数据更容易上传、复用和进行基准测试，研究人员、初创公司以及大型机构，正在公开那些几年前还只能封存在实验室里的真实机器人数据集。 主要贡献者包括 NVIDIA、LeRobot 计划，以及一个快速壮大的创客社区。这一浪潮也得益于更低成本的视频存储、更完善的工具链，以及开源 AI 文化向物理世界的外溢。 而这一切至关重要：开放的机器人数据正在显著降低行业门槛、加速“边做边学”的过程，并推动通用型机器人和类人机器人更快取得进展。 机器人规模化的关键，不仅在硬件，更在于共享数据。 Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, @huggingface datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include @nvidia, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data.