通过遥操作机器人来采集数据成本高昂且要求严苛：首先，它需要一台机器人；其次，取决于所采用的遥操作方法，若数据采集耗时数小时，则对用户而言可能极为繁琐，并涉及显著的硬件与后勤挑战。这使得该方法难以扩展以覆盖任务与环境的广泛多样性。但采集机器人数据并不一定需要机器人——仅需一只人手、一个夹持器、一台相机，以及一种能够恢复人手运动六自由度（6-DoF）轨迹的方法。

Teleoperating a robot to collect it can be expensive and demanding: first of all, it requires a robot. And depending on the teleoperation method, data collection can be tedious for the user if it takes hours and involve significant hardware and logistical challenges. That is difficult to scale with the wide variety of tasks and environnements required. But you don't need a robot to collect robot data. Just a human hand, a gripper, a camera, and a way to recover the 6-DoF trajectory of what the hand did.

远程操控机器人来采集数据可能成本高昂且要求严苛：首先，它需要一台机器人；其次，取决于所采用的遥操作方式，若数据采集耗时数小时，并涉及大量硬件与后勤挑战，则对用户而言将十分繁琐。这种方式难以应对任务类型与环境种类的广泛多样性。但采集机器人数据并不一定需要机器人——仅需一双手、一个夹爪、一台相机，以及一种能够恢复手部六自由度（6-DoF）运动轨迹的方法即可。记录一次示范动作，便获得可供机器人学习的数据。而我们的更大目标是：若记录示范动作如同拍摄视频一样简单，那么任何人都能参与贡献。我们希望 Grabette 能够催生一个规模庞大、开放共享、协同共建的操作技能数据集——这是任何单一实验室都无法独自构建的。 Grabette 直接借鉴了斯坦福大学提出的通用操作接口（Universal Manipulation Interface, UMI）：一种配备鱼眼镜头相机的手持式夹爪，可在真实环境中录制操作示范，利用 SLAM 恢复相机运动轨迹，并基于这些数据训练视觉运动策略（visuomotor policies）。我们已持续开发 Grabette 数月，目前认为其已具备足够可用性，值得与公众分享。我们非常高兴此刻将其正式发布！ Grabette 是一款手持式夹爪设备，集成了重建操作示范所需的所有传感器。它搭载两台相机，各自承担明确分工。这种职责分离设计是有意为之：低成本广角鱼眼相机为策略模型提供富含上下文信息的、类似手腕相机视角的画面；而 RGB-D 相机则负责高鲁棒性的六自由度位姿跟踪任务。 本次发布使任何人皆可快速完成从“我想演示一项任务”到“我已拥有可用于训练的数据集”的全过程，且无需任何先验专业知识。只需在浏览器中打开 Grabette 仪表板，选择拟加入数据集的演示片段，单击一次，后处理即自动启动。

Teleoperating a robot to collect it can be expensive and demanding: first of all, it requires a robot. And depending on the teleoperation method, data collection can be tedious for the user if it takes hours and involve significant hardware and logistical challenges. That is difficult to scale with the wide variety of tasks and environnements required. But you don't need a robot to collect robot data. Just a human hand, a gripper, a camera, and a way to recover the 6-DoF trajectory of what the hand did. Capture the demonstration and you have data a robot can learn from. And that's the bigger goal: if recording a demonstration is as easy as shooting a video, anyone can contribute. We want Grabette to seed a large, open, collaborative manipulation dataset. One no single lab could ever build alone. Grabette is directly inspired by the Universal Manipulation Interface (UMI) from Stanford: a handheld gripper with a fisheye camera that records demonstrations "in the wild", recovers camera trajectories with SLAM, and trains visuomotor policies from them. We have been developing Grabette for months, and we feel it has become usable enough to share. We are excited to share it now with you! Grabette is a handheld gripper instrumented with everything needed to reconstruct a manipulation demonstration. It carries two cameras, each with a distinct job. Splitting the two roles is deliberate: the cheap wide fisheye gives the policy the context-rich, wrist-camera-style view it needs, while the RGBD camera does the heavy lifting of robust 6-DoF tracking. This release enables anyone to go from "I want to demonstrate a task" to "I have a training-ready dataset" quickly and without prior expertise. Open the Grabette dashboard in your browser. Select the episodes you want to add to the dataset, and with one click, post-processing begins.