Google DeepMind 发布 Gemini 4 Argon，可从视频教会 Apollo 人形机器人新技能
Google DeepMind uses Gemini 4 Argon to teach Apptronik’s Apollo humanoid new skills from video
Google DeepMind 发布 Gemini 4 Argon，并演示该模型通过观看视频学习物理任务，再迁移到 Apptronik 的 Apollo 人形机器人。在内部测试中，Argon 观看投掷沙包视频后分析环境、映射空间坐标并计算全身关节动作，自主生成仿真代码，Apollo 在真实试验中连续命中七次。Google DeepMind 称这套 Video-to-Sim-to-Real 流程可把原本需要数周的仿真建模压缩到数小时，Argon 在具身推理基准 ERQA 上得分 87%，模型已通过 Google 的 AI 产品和 Gemini API 开放。
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演示展示了从原始视频到仿真代码再到真机执行的完整链路，可据此判断通用模型在机器人训练中的介入程度。
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Google DeepMind has demonstrated a new robotics training technique in which its Gemini 4 Argon AI model learns a physical task by watching video and then transfers the resulting behavior to Apptronik’s Apollo humanoid robot.
The demonstration forms part of the launch of Gemini 4 Argon, Google’s latest frontier AI model, which the company says combines visual-semantic reasoning, physical understanding and autonomous code generation for robotics applications.
In an internal test, Argon was shown raw video of a person playing cornhole. The model analyzed the environment, mapped its spatial coordinates and calculated the whole-body joint movements required to perform the task.
Argon then autonomously generated simulation code before the learned behavior was transferred to Apptronik’s Apollo humanoid.
According to Google DeepMind, Apollo subsequently landed seven consecutive shots during a real-world trial.
The company describes the approach as a “Video-to-Sim-to-Real” pipeline and says it can reduce a robotics simulation process that would normally require weeks of manual work to a few hours.
Google DeepMind says: “This ‘Video-to-Sim-to-Real’ pipeline compresses weeks of manual simulation modeling into a few hours, transforming ordinary videos into a scalable, infinite training ground for robotics.”
The demonstration is intended to show how general-purpose AI models could reduce the amount of manual engineering required to teach robots new physical skills.
Google DeepMind also tested Argon on its Embodied Reasoning benchmark, or ERQA, which measures a model’s ability to reason about physical and spatial problems in 3D environments. The company says Argon achieved a score of 87 percent.
Gemini 4 Argon is being positioned more broadly for complex engineering, scientific, coding and business tasks. Google says it has already used the model for projects ranging from optimizing large software codebases to quantum computing, nuclear fusion and life-sciences research.
The model is now being made available through Google’s AI products and Gemini API, with Google presenting the robotics work as part of a wider effort to apply advanced AI reasoning to physical systems.