机器人训练数据，AI 自己造，成本降到十分之一
训练机器人干活，过去得靠人远程操控它一遍遍演示，一次一个任务，又慢又贵。这篇让一个会写代码的 AI 代理接管这份工作：给它一个任务描述和一段机器人在真实场景的校准视频，它就在仿真里把任务重建出来——自己写场景、写控制程序、写评判标准，然后生成逼真的视频数据，直接拿去训练机器人。在五个真实任务上，只用 AI 造的数据训练的机器人，表现和用真人演示数据训练的持平(93 对 84 次成功);更关键的是，换个桌布和灯光，AI 数据训练的还能保持 87 次成功，真人数据训练的掉到 46。成本上，一个任务从零适配平均只要 21 美元 API 费，之后每段数据 0.8 美元，一小时三视角数据总共 107 美元，比真人远程操控在中国(117 美元)和北美(290 美元人工)都便宜。它不是你明天就能用上的，但机器人公司最贵的瓶颈——数据采集——正在被 AI 自己解决，这个方向值得盯着。
📄 原文摘要(英文)
Teleoperation remains the default way to collect robot demonstrations, but every episode costs robot and operator time, and every new task starts from scratch. We ask whether a coding agent can take over the teleoperator's job. In our DataFarm, the agent receives a data request and one calibration episode of the robot in the target scene and rebuilds the task in simulation: it writes the scene, an expert controller, and a success judge. The simulator supplies exact actions, and a video model conditioned on real images renders each rollout as photographic video, generating all camera views together consistently. The output is real-ready data: synchronized camera views labelled with the robot's actions, on which a policy can be trained directly and deployed on the real robot. Independent judges certify the expert and every rendered episode before admission. On five real-robot tasks, policies trained only on farm data match policies trained on as many teleoperated episodes (93 vs. 84 successes in 100 trials), and under an unseen tablecloth and coloured light they keep 87 successes where the teleoperation-trained policies fall to 46. Adapting a task is fully automatic and costs \21 of API usage on average, paid once per task, plus 5 for a render contract; every further episode costs \0.80 on average for simulation, rendering, and review, with no robot or operator. One hour of three-view data for a task costs 107 on average, these costs included, slightly below a quote of \117 for teleoperation in China and below 290 in labour alone in North America.