AI学会做实验了？但只限在虚拟实验室里
现在的AI能帮你读论文、写代码，但真要它动手做实验——比如量液体、摇试管——它连透明液体都认不出。这篇论文的团队造了一个虚拟实验室，让AI在里面反复练习：先让大模型学会“动作词汇”（比如“抓取”“倾倒”），再用流匹配技术把动作连成流畅的操作。在模拟测试中，它的成功率比现有模型高出一截。但别急着让它进真实实验室——它目前只在虚拟环境里练过，真实世界的玻璃仪器、液体折射、手抖误差都还没碰过。这是AI从“动脑”到“动手”的一个前沿信号，但离替你站实验台还远。
📄 原文摘要(英文)
Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach. AI can help read literature, generate hypotheses, and plan protocols, yet the execution of those protocols at the bench still requires a human operator. Vision-Language-Action (VLA) models provide one possible interface between written protocols and robot execution, but existing policies are trained mostly on household and tabletop demonstrations and rarely encounter the instruments, transparent liquids, or fixed protocol workflows found in scientific laboratories. Closing this gap requires both laboratory-specific supervision and a unified learning framework that can accommodate the diverse robot embodiments used to execute experimental protocols. We therefore identify data and embodiment as central bottlenecks alongside model design. To address the data side, we build RoboGenesis, a simulation-based workflow and data engine that composes configured laboratory workflows from atomic skills, validates and filters rollouts, and exports structured demonstrations across supported robot profiles. On the policy side, we present LabVLA, trained with a two-stage recipe: FAST action token pretraining first makes the Qwen3-VL-4B-Instruct backbone action aware before any continuous control is learned, and flow matching posttraining then attaches a DiT action expert under knowledge insulation. On the LabUtopia benchmark, LabVLA achieves the highest average success rate among all evaluated baselines under both in-distribution and out-of-distribution settings.