Apple 论文：Claude Opus 5.5 直接控制机械臂，零样本成绩超 VLA 专用模型
苹果没训练任何机器人模型，只把 Claude Opus 5.5 接上摄像头和机械臂，RoboLab-120 上零样本就拿到 85.6%，比专门训过的 VLA 高了近一倍。做机器人或具身智能的值得看看这个思路，缺点是实时性还跟不上。
Apple 发布论文介绍 AMP1 测试框架，让 Claude Opus 5.5、Gemini 4 Argon 这类通用大模型直接控制机器人机械臂，无需任何演示数据或任务训练。框架只提供相机画面、代码执行接口和底层运动指令，模型自行发明抓取与放置策略。
在 RoboLab-120 基准上，AMP1 零样本成功率达到 85.6%，而训练过演示数据的最佳 VLA 模型为 49.2%，最佳 world-action 模型仅 42.9%。在真实 Franka 机械臂的 108 次试验中成功 104 次。主流 VLA 方案依赖录制演示训练，指令一旦变化就容易失效；AMP1 的做法是用通用模型的推理能力替代专用策略训练。
短板在速度：机器人每个回合大部分时间在等待模型输出。这篇论文对机器人开发者意味着，具身智能可能不一定要从海量演示数据训练开始。
New Apple paper shows that a general-purpose AI model like Claude Opus 5.5, given camera views and direct control of a robot arm, handled objects more reliably than robot models trained on recorded demonstrations.
New Apple paper shows that a frontier AI model directly controlling a robot arm beats specialist robot policies, with no demonstrations or task training.
Most robot manipulation work trains specialist policies, like vision-language-action models, on demonstrations, and they break easily when instructions change.
Their harness, AMP1, gives a model like Claude Opus 5.5 or Gemini 4 Argon camera views, code execution, and direct motion commands in a loop. It has no built-in grasping or placing skills, so the model invents its own strategies.
On RoboLab-120, AMP1 succeeded in 85.6% of episodes zero-shot, against 49.2% for the best VLA and 42.9% for the best world-action model. On a real Franka arm it succeeded in 104 of 108 trials, though the robot waits much of each episode for the model.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Claude Opus 5.5, Gemini 4 Argon, VLA, 具身智能.
Source attribution: curated from rohanpaul_ai (@rohanpaul_ai).
