Google 发布面向机器人应用的 Gemini 4 Argon 模型

OCT. 13, 2026

Gemini 4 Argon 标志着在物理理解、代码生成和视觉-语义推理上的跨越式进步——解锁了机器人技术栈中的全新工作流，并加速迈向物理 AGI 的进程。

你可以在 具身推理（ERQA）基准测试 上看到这种能力上的飞跃，该基准用于测试模型在三维环境中理解并解决物理空间问题的能力。仅依靠纯推理，Argon 即取得了 87% 的得分，超越了所有其他前沿模型。

作为一个通用的物理推理模型，Argon 可应用于整个机器人生命周期。在本文中，我们将分享 Argon 在四个关键领域的代表性提示词（prompt）：

• 机器人控制 → 借助 MuJoCo 练习的移动操控、零样本机器人控制，以及对视频世界模型的闭环操控。
• Real2Sim → 通过机器人相机画面、第一人称人类视频或手绘纸质草图重建经过物理标定的仿真孪生体。
• 物理理解 → 智能体多模态事后标注，包含对灵巧操作的细致理解、密集价值函数与进度估计。
• 研究与自我改进 → 通过仿真改进动作，以及在硬件上的迭代自我改进。

用 Argon 解锁机器人控制

1. Spot 移动操控：借助 MuJoCo 练习将物品丢进垃圾桶

Argon 控制一台 Boston Dynamics Spot® 四足机器人（12-DoF 腿部 + 6-DoF 机械臂）来学习一项新技能：踩下脚踏式垃圾桶，把易拉罐扔掉。Argon 先构建一个带关节的 MuJoCo 数字化脚踏式垃圾桶孪生体来练习动作，再实际踩下脚踏，把易拉罐投入打开的桶中。

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Spot 移动操控 —— Real-to-Sim-to-Real 脚踏式垃圾桶丢弃：Spot 拾起易拉罐，查看腕部相机，在 MuJoCo 中练习三足三角平衡，踩下脚踏，确认夹爪在打开的桶口居中，然后将易拉罐投入桶内。

提示词：

There's a Diet Coke can behind you and another on the black table on your left.
Use the spot-mobile-manipulation skill to pick them up and trash them in the black foot-pedal bin.
Before stepping down on the pedal, verify in the front cameras that your front-right foot target is centered on the pedal, and use a 2-agent verification swarm (foot-verifier and hand-verifier) to verify both the foot-pedal step and the 3D hand-in-bin alignment before releasing.

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2. 零样本控制

Argon 控制一支简单的机械臂，将倒下的杯子扶正并把揉皱的纸巾放进杯内。Argon 通过视觉坐标系框架和动作原语进行推理，分多轮完成该任务。

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左：第三人称真实机器人视角；右：经过标定的腕部相机，叠加了 2 cm 世界坐标系投影以及夹爪闭合十字线。

提示词：

[Attached: wrist_cam.png, obs.json]

Task: Get the robot arm to reorient the mug upright and put the napkin inside the mug.

## Execution Discipline
- Simply output 1 sentence observation followed immediately by your `curl` command (with `# Plan:` on Turn 1 and `# Thought:` on every turn). Everything needed is in `obs.json` and the calibrated reticle/grid drawn on `wrist_cam.png`.

## Visual Coordinate Reference (Annotated on wrist_cam.png)
- `+X` (UP in image): Away from robot base | `-X` (DOWN): Towards robot base
- `+Y` (LEFT in image): World left         | `-Y` (RIGHT): World right
- `+Z`: Height above table                | `Yellow numbers`: World (X, Y) in centimetres (2 cm grid at `grid_z`)
- `Cyan + crosshair`: Current 3D pinch center
- `Cyan [ + ] brackets`: Current jaw opening and closing line
- `Green line`: Current object long-axis orientation (`yaw_deg`: 0° = vertical along +X, 90° = horizontal along +Y)
- `pitch_deg = 90°`: Straight down (-Z) top-down grasp/place; `pitch_deg < 60°`: Tilted coaxial approach along finger axis

## Closed-Loop Turn Workflow
1. Inspect current state & view `wrist_cam.png`:
   `curl -s http://127.0.0.1:8081/obs` (returns `ee_pos`, `holding`, `status`, `workspace`, `wrist_cam_path`).
2. Select VLM call granularity upfront on Turn 1:
   - Coarse (2–4 calls): `look_at` -> `grasp` -> `place` -> `done`
   - Pre-Grasp Hover Verify (4–7 calls): `look_at` -> `goto(z=0.04)` (verify cyan `[ + ]` brackets straddle target) -> `grasp` -> `place_until_contact` -> `done`
   - Fine Visual Servoing (8–20 calls): `look_at` -> `grasp` -> pre-pose `goto` -> closed-loop `move_by` / `move_until_contact` -> `done`
3. Send motion primitives (`POST /plan`) and verify visually after every turn in the updated `wrist_cam.png`. If `status == "grasp_failed"`, call `robot.look_at(...)` to re-observe and recover closed-loop.

## Code-as-Policies Primitives (`POST http://127.0.0.1:8081/plan`)
robot.look_at(x=0.35, y=0.13, z=0.14, grid_z=0.015)
x, y = robot.deproject(u=0.52, v=0.48, z_plane=0.015)
robot.grasp(x=x, y=y, z=0.015, yaw_deg=-35, pitch_deg=90)
robot.place(x=0.24, y=0.06, z=0.075)
robot.place_until_contact(x=0.24, y=0.06, z=0.015, max_lag=0.012, relief=0.002)
robot.goto(x=0.28, y=0.05, z=0.04, arc_z=0.14, pitch_deg=25, azimuth_deg=90, roll_deg=0, speed=0.10)
robot.move_by(forward=0.025, dz=-0.002, dpitch_deg=-3, droll_deg=5, speed=0.04)
robot.move_until_contact(dz=-0.06, speed=0.03, max_lag=0.012, relief=0.002)
robot.open_gripper() / robot.close_gripper() / robot.home() / robot.done()

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3. 全模态控制

Argon 在 Gemini Omni 世界模型中操控机器人，将五块散落的 Scrabble 字母拼字块（G, R, A, N, O）重新排成整齐的一行，拼出 A - R - G - O - N。Argon 并未指定单一的静态提示，而是在视觉闭环中进行操控：它编写带有时间戳的动作提示词，检查中间的推演关键帧，并不断优化提示词。

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灵巧手迭代式视频世界模型操控：33 步受控视频推演，拼出 A-R-G-O-N。

提示词：

Goal: {task_goal}

You are steering a video world model (Gemini Omni) in an iterative visual feedback loop. Inspect the attached frames from the latest rollout and write the next timestamped action prompt for the video world model:
- Break complex motions into one simple, single-direction action at a time.
- Keep inactive hands retracted and avoid blocking the camera's view of objects in the workspace.
- Explicitly state which areas of the workspace become empty after an object moves.
- Use [MM:SS.mmm] timestamps for each action beat and wrap your output in <omni_prompt> tags.

Example format (sample timestamps only, not the full prompt):
<omni_prompt>
[00:00.000] Character A's left hand rests at the side while the right hand reaches toward the target object.
[00:02.500] Character A's right fingertips push the object smoothly into the center of the table, leaving its starting area empty.
[00:05.000] Character A's right hand opens and retracts to the side so all placed objects remain in clear view of the camera.
</omni_prompt>

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用 Argon 解锁 Real2Sim

1. 机器人视角到仿真

Argon 观察一段真实的机器人遥操作片段，并生成数字孪生；它获取或创建仿真资产，使其在与灵巧手接触时外观与行为都与真实物体一致。Argon 通过迭代的视觉推理循环，不断调整资产与场景，使其视觉与接触事件与真实录制数据相匹配。

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左侧（真实视角）：同步的真实机器人相机画面；右侧（仿真）：由 Argon 重建并校准的 3D MuJoCo 场景。

提示：

[Attached: robot_rollout_episode]

Build a MuJoCo scene of the robot and reconstructed task objects. When each episode is replayed frame by frame, by driving the robot joints toward the logged observation.state with joint-space PD.

Iterate using two verification objectives:
(1) Visual match: MuJoCo renders from the head_left, left_wrist, and right_wrist cameras line up with, and look like, those real observations.
(2) Physical match: the simulated contact force/torque on each Sharpa fingertip, in the sensor frame, matches the logged tactile_force in timing, magnitude and direction.

What you can tune happens before simulation starts:
(1) Static object assets: realistic geometry, collision shapes, mass, inertia, friction and joints, with appearance close to the real objects, plus the initial object poses at t = 0.
(2) Offline-planned robot commands, changed only in frames where the real fingertips are near an object. Each command must stay within 0.55 rad (arm joints) and 0.65 rad (hand joints) of the logged teleop trajectory. Everywhere else, replay the logged trajectory unchanged.

No sim cheating: task objects may move only through real MuJoCo contact with the robot hands and the environment (mj_step).
Not allowed after t = 0: changing MjModel parameters; applying external forces or torques to objects; welds or equality constraints; feedforward torques beyond joint-space PD; setting or rolling back object qpos/qvel; disabling or excluding collisions; reporting contact forces that don't come from the MuJoCo solver (for example, copying the logged tactile readings).

Immediately present the reconstructed scene and rollout results as an interactive HTML session viewer with one page per episode, showing:
- At the bottom: A synced timeline (play, scrub, speed control) that drives every panel.
- On the left panel: An interactive 3D Blender scene showing the simulated robot and objects, showing contact points and force arrows as replay progresses.
- On the right panel: Real vs. sim camera views side by side for all 3 cameras. Real vs. sim finger force visualizations.

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2. 从人类视角到仿真

给定一段操作任务的第一视角人类视频，Argon 识别任务相关对象，将粗糙的边界框替换为针对凹面容器的复合凸分解网格，并在 MuJoCo 接触物理中将人类手部轨迹重定向到配备 22 自由度五指手、整机 76 自由度的双臂人形机器人上。

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人类视角到仿真 — 第一视角人类视频到灵巧人形机器人孪生：左侧显示输入的第一视角人类 RGB 视频；右侧显示同步的 500 Hz MuJoCo + Blender Cycles 多视角仿真推演。

提示：

[Attached: egocentric_human_video.mp4, candidate_sim_views.png, hand_3d_keypoints.json]

You are building a physically solvable 500 Hz MuJoCo manipulation environment (`scene.xml`) and studio Blender render that faithfully reproduces the tabletop task shown in `egocentric_human_video.mp4` on a bimanual humanoid robot with 22-DoF dexterous five-fingered hands:

1. Scene Reconstruction & Camera Calibration:
   - Identify only the manipulation-relevant objects (the grasped target object and the receiving container or landmark) and triangulate their 3D tabletop coordinates from the egocentric camera intrinsics/extrinsics.
   - Replace crude bounding boxes with compound convex-decomposed 3D meshes so concave containers (nested bowls, recessed wooden trays, open bins) support physical interior placement.

2. 500 Hz Dexterous Motion-Fitting Stress Test:
   - Retarget the 3D human wrist and finger keypoints to the humanoid's 7-DoF arm and 22-DoF hand joints, and step the trajectory in MuJoCo at 500 Hz (dt = 0.002s) with zero kinematic weld constraints.
   - Verify opposing thumb-and-finger force closure, clean object lift (> 2 cm), and stable final placement inside or beside the target landmark.

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3. 从草图到仿真

借助 Argon，开发者只需拍摄一张粗略的纸笔草图——图中显示一台 Apollo 2 人形机器人沿抛物线轨迹掷球，击倒桌面上按 3-2-1 堆叠的箱子——Argon 即可自动构建 GPU 加速的 mujoco_warp 场景，合成平衡的 76 自由度全身五指投掷轨迹，在闭环物理中验证 6/6 目标击倒，并导出 Blender 4.5 Cycles 渲染结果。

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从草图到仿真 — 手绘草图到灵巧人形机器人轨迹与 Cycles 渲染。

提示：

[Attached: hand_drawn_throw_sketch.jpg]

Translate this hand-drawn sketch into a physically verified humanoid simulation in `mujoco_warp` and a raytraced Blender Cycles video:

1. Scene & Kinematic Setup from Sketch:
   - Parse the spatial layout from `hand_drawn_throw_sketch.jpg`: place the 76-DoF humanoid (32-DoF body equipped with two 22-DoF five-finger dexterous hands) at the origin facing a table holding a 3-2-1 pyramid of 6 rigid target boxes, with a projectile sphere grasped in the right five-finger hand.

2. Whole-Body Balance & Dexterous Throw Synthesis:
   - Synthesize a 50 Hz whole-body throwing trajectory (140 steps, 2.80 s) across four phases: (1) stable stance & 5-finger power grasp closure, (2) torso/shoulder wind-up with ankle-hip Center-of-Mass compensation, (3) forward whipping acceleration, and (4) synchronized 22-DoF five-finger opening at peak hand velocity so the ball launches on a ballistic arc into the pyramid's second-tier center of mass.
   - Verify in `mujoco_warp` contact physics that the humanoid maintains upright balance (< 3 cm pelvis drift) and knocks down all 6 target boxes.

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借助 Argon 释放物理理解能力

1. 后见标注与数据质量

Argon 对灵巧操作具有精确的物理理解，能够从视频中提供详细的分割与文本标注。Argon 能够识别出较差的操作片段，例如停顿、抓取不稳与失误。给定一段时长 136 秒的双臂 ALOHA 2 机器人将一张方形纸折叠为折纸狐狸的俯视视频，Argon 生成一个时间对齐的四级后见标注层次：片段目标（G1）、6 个连续的语义子任务（G2）、17 个接触级原子基元（G3），以及在失误与重试过程中将任务进度保持平稳的自动质量控制（QC）重加权。

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Argon 多粒度后见与遥测 HUD。

提示：

[Attached: Aloha2_Origami_20250309.mp4]

Produce a time-aligned multi-granularity hindsight annotation JSON with four levels:
1. G1_episode_instruction: Concise imperative sentence describing the overall manipulation goal.
2. G2_subtasks: Contiguous semantic subtask phases specifying which arm(s) anchor vs. fold/rotate the object.
3. G3_atomic_primitives: Fine-grained contact-level primitives (3–15s) aligned with grasp closure, crease sweeps, release, or retry attempts.
4. quality_control_and_reweighting: Hold task_progress_pct flat during non-progressing slip/delamination retry loops and downweight them (0.15x–0.25x) for Behavior Cloning.

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2. 找出不同的球

为用机器人重现经典的 12 球天平称重谜题，Argon 构建了一个双臂 Franka FR3 MuJoCo 环境，包含 12 个外观相同的台球（其中一颗秘密地更重或更轻）以及一副铰接式 Roberval 双盘天平。然后它编写一个仅依赖视觉的闭环求解器，通过立体 RGB 相机对球进行三角测量，视觉读取每次称重后天平的倾斜，并在 3 次称重内将不同的球隔离到重球（红色）或轻球（蓝色）托盘中。

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找出异常球（纯视觉闭环求解器）：双臂 Franka FR3 在金色/青色托盘上称量候选球，读取立体秤倾角，并在 3 次称量中将确认的重质异常球分离至红色托盘。

提示：

# Prompt 1 — Physics-Accurate MuJoCo Environment & Benchmark Generation
Make a MuJoCo environment where a dual-arm robot (two 7-DoF Franka FR3 arms with parallel-jaw grippers on a shared tabletop) is tasked to solve the classic 12-ball balance scale weight puzzle from https://www.mathsisfun.com/pool_balls_solution.html.
1. Create 12 visually indistinguishable pool balls in a 2x6 starting rack (11 standard 150 g, 1 odd ball either 270 g or 30 g).
2. Build a functional two-pan Roberval balance scale at the center of the table with parallelogram equality constraints (`mjEQ_JOINT`) so the Left (Gold) and Right (Teal) pans stay horizontal, plus Red (Heavy) and Blue (Light) submission trays.
3. Track distinct settled weighings (`weighing_count`) and reward `1.00` for isolating the true odd ball in the correct Heavy/Light tray within <= 3 weighings.

# Prompt 2 — Closed-Loop Vision-Only Solver
Implement an independent closed-loop policy relying strictly on stereo RGB camera inputs (`head_camera_left_rgb_image`, `head_camera_right_rgb_image`) and robot proprioception (`add_privileged_task_observations=False`):
1. Segment the 12 balls, Gold/Teal pans, and Red/Blue trays in both stereo views and triangulate their 3D coordinates.
2. Visually read the balance scale tilt after each weighing from stereo vertical pixel disparity (+1 = left heavier, -1 = right heavier, 0 = balanced).
3. Rotate the gripper wrist yaw by 90 degrees when grasping/placing inside the narrow scale pans to clear the raised rims, and isolate the odd ball into the Red or Blue tray within <= 3 weighings.

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3. 价值函数

Argon 是面向机器人任务的通用价值函数。在来自人类 UMI 数据集的一段 60 秒双手分拣圆环回合中评估时，Argon 在接触过渡前后迭代地检视同步的头部、左手夹爪和右手夹爪相机画面，以生成密集的价值函数/任务进度预测，并将轨迹分解为 28 个具有正向、中性和负向进度斜率的段。

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价值函数：同步三相机 UMI 回放（头部、左手夹爪、右手夹爪），与动画任务进度曲线及双手分段标注联动。

提示：

[Attached: umi_head.mp4, umi_left_gripper.mp4, umi_right_gripper.mp4]

Look at 3 videos of human wearing a gripper performing activities from head, left, right gripper views. Look at the videos thoroughly and give a detailed plot of progress at the task (with positive, neutral, negative slope), identifying important segments in the video and labeling the activities. Look at individual frames or subsegments of the video if necessary to get a better understanding.

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借助 Argon 解锁研究与自我改进

1. 通过仿真改进动作

Argon 能在仿真中生成新数据，以提升通用视觉-语言-动作（VLA）模型的性能。从真实桌面的单张 RGB 照片出发，Argon 自动构建一个经过校准的 MuJoCo 仿真孪生环境，定义新任务，并合成一个闭环运动生成器，在仿真中采集 4,500+ 域随机化的回合——将真实世界物理机器人的成功率从 56% 提升至 75%，并将完成时间从 35 秒缩短至 24 秒。

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通过仿真改进动作：提升真实世界评估基准（成功率从 56% 提升至 75%，完成时间从 35 秒缩短至 24 秒）。

提示：

[Attached: real_tabletop_photo.jpg]

You are given a single overhead RGB camera photo (`real_tabletop_photo.jpg`) of a bimanual robot workspace (two 7-DoF Franka FR3 arms with parallel-jaw grippers and 4 RGB cameras) facing a table arranged with photobooth party props.
1. Reconstruct procedural 3D meshes and compound MuJoCo collision primitives for all 9 party props matching their real-world dimensions, colors, and resting poses, including spring-hinged deformable crown panels for the soft plush cap.
2. Define a 77-task single-arm and bimanual curriculum (pick-and-place, uprighting, directional pushing, mid-air handovers, bimanual scoop-and-push, and pipelined table clearing).
3. Synthesize a closed-loop IK trajectory generator across ±10 cm pose randomization, verify `reward == 1.0` in MuJoCo physics, and export 4,500+ episodes for VLA fine-tuning.

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2. 视觉抓取放置的自我改进

在一台简单的 6 自由度机械臂上，Argon 通过试错与自我反思，在抓取放置任务中迭代地自我提升执行速度与放置精度。

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SO-101 视觉引导抓取放置——跨第 1 → 2 → 3 次迭代的硬件自我改进：第 1 次迭代（58.9 秒），第 2 次迭代（22.9 秒，放下时擦过箱体边缘），第 3 次迭代（17.4 秒，正中落下）。

提示：

# Stage 1 — Measure Objects & Dry-Run Plan (No Hardware Motion)
Stage 1: measure objects — find the objects and plan the whole motion. The arm does not move in this stage.

A. I put a LEGO brick and a box on the table in front of the arm, and the wooden box to one side. Find the brick and the wooden box in the image. And also in the real world. Show this in an annotated image.

B. A dry-run plan (no hardware). Write the planner to pick and place the lego block. Print the plan table, draw all poses onto the overhead image, and stop. Do not move the robot.
Report with evidence.

# Stage 2 — Pre-Pick Hover & Wrist-Camera Verification
Stage 2: Put the gripper above the brick without touching it, and verify with the wrist camera that the arm and the vision agree. Please record executions with both cameras (compressed video is OK).

# Stage 3 (Autonomous Pick-and-Place → Self-Improvement → Optimization):
1. Execute autonomously: Turn the Stage 1 dry run into a real grasp, lift, and place into the wooden box. Run it yourself subject to safety rules from AGENTS.md.

2. Self-improve: When an attempt fails, diagnose it yourself. Review your own logs and wrist/overhead footage, find the root cause, check the fix in a dry run, and retry.

3. Optimize on feedback: Once it succeeds, make it at least 2× faster within safety limits (rule R5) unless I authorize more, or give feedback.

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3. 在 MuJoCo WebAssembly 中一键复现机器人学论文

只需对每篇论文给出一个提示，Argon 即可阅读机器人控制论文及任何已发布的检查点，将物理模型与闭环控制器（从 DeepLoco 中的 7 网络分层运动控制，到 HUSKY 中的 50 Hz PPO 智能体，再到 Whole-Body MPC 中的 Web Worker iLQR）移植到独立的 MuJoCo WebAssembly 浏览器实验平台，并在无头 Chromium 中进行验证。

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五篇在浏览器中实时运行的腿足与仿人里程碑论文（MuJoCo WASM）：机器人足球 OP3 1v1、DeepLoco、大规模并行深度强化学习 ANYmal C、Whole-Body MPC Go2，以及 HUSKY G1 滑板（来自 Argon 复现的 68 个交互式浏览器沙盒）。

提示：

[Attached: paper.pdf, optional author_checkpoints/ or urdf_assets/]

Reproduce the paper "<Paper Title & arXiv ID>" from scratch in MuJoCo WebAssembly and deploy a self-contained browser lab + visual essay:
1. Match the paper's exact physics timestep, actuator gains (kp/kv), torque limits, and kinematic constraints in MuJoCo XML, and run real neural inference or Web Worker iLQR in JavaScript at the paper's exact control rates.
2. Build `index.html` (Spatial UI simulator with camera presets, live telemetry plots, and disturbance injection) and `scrollytelling.html` (interactive visual essay).
3. Run a headless Chromium (Playwright) verification loop asserting 0 console errors and non-zero task progress over 300+ frames.

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借助 Argon 推动具身智能发展

Argon 融合了编程、视觉感知、物理仿真和机器人具身控制。通过对多视角视频、3D 几何、接触力学和本体感知遥测数据进行原生推理，Argon 不仅能理解场景中的物体是什么，还能理解它们如何与智能体进行物理交互以完成全身灵巧操作任务。Gemini 4 Argon 是一个重要里程碑，加速了通往物理 AGI 的道路。通过构建这一核心智能，我们的目标是让机器人能够与人类并肩工作，解决复杂的挑战。

快速开始

您今天就可以在 Google 的开发者平台上开始使用 Gemini 4 Argon 进行构建：

• Google AI Studio：面向 Google AI Ultra 订阅用户开放。
• Gemini API：在 AI Studio 中通过 Gemini API 的付费版本即可使用。在 Interactions API 中使用 gemini-4-argon，并在 google-genai SDK 中获得首发支持。
• Google Antigravity：面向 Google AI Ultra 订阅用户开放。要开始构建，请在 Google Antigravity 中将 Gemini 4 Argon（gemini-4-argon）选为活动模型，用于复杂规划、/goal 运行和多智能体编排。

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