Google DeepMind 推出面向机器人任务的 Gemini 4 Argon

2026年10月13日

Gemini 4 Argon 在物理理解、代码生成和视觉语义推理方面实现了跨越式提升，为机器人技术栈的各个环节开启了新的工作流程，并加速迈向物理 AGI。

你可以在具身推理（ERQA）基准测试中看到这一能力飞跃，该测试评估模型理解和解决 3D 环境中物理与空间问题的能力。仅凭推理，Argon 就取得了 87% 的得分，超越所有其他前沿模型。

作为通用物理推理模型，Argon 可应用于机器人的整个生命周期。在本文中，我们分享了 Argon 在四个关键领域的代表性提示词：

• 机器人控制 →通过 MuJoCo 练习实现移动操作、零样本机器人控制，以及视频世界模型的闭环引导。
• Real2Sim →根据机器人摄像头画面、第一人称视角的人类视频或纸上手绘草图，重建经过物理校准的仿真孪生体。
• 物理理解 →智能体式多模态事后标注，包括对灵巧操作的细致理解、密集价值函数和进度估计。
• 研究与自我改进 →通过仿真改进行动，并在硬件上进行迭代式自我改进。

用 Argon 解锁机器人控制

1. Spot 移动操作：通过 MuJoCo 练习将物品放入垃圾桶

Argon 控制 Boston Dynamics 的 Spot® 四足机器人（12 自由度腿部 + 6 自由度机械臂）学习一项新技能：踩下脚踏式垃圾桶的踏板，将汽水罐扔进去。Argon 为脚踏式垃圾桶创建了一个带关节结构的 MuJoCo 数字孪生体，先练习动作，再踩下真实踏板，将汽水罐投入打开的垃圾桶。

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Spot 移动操作——从现实到仿真再到现实的脚踏式垃圾桶投放：Spot 拿起汽水罐，查看腕部摄像头，在 MuJoCo 中练习三腿支撑平衡，踩下脚踏板，确认手部位于打开的垃圾桶中央，然后将汽水罐放入桶内。

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 控制一只简单的机械臂，将倒下的马克杯扶正，并把揉皱的餐巾放进去。Argon 借助视觉坐标辅助框架和动作基元进行推理，经过多轮操作完成这项任务。

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左：第三人称视角的真实机器人画面；右：经过校准的腕部摄像头画面，叠加投影的 2 cm 世界坐标和夹爪闭合瞄准标记。

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. Omni 控制

Argon 引导 Gemini Omni World Model 中的机器人，将五块散落的 Scrabble 字母牌（G, R, A, N, O）重新排列成整齐的一行，拼出 A - R - G - O - N。Argon 并非只指定一条静态提示词，而是在视觉闭环中进行引导。它编写带时间戳的动作提示词，检查中间生成序列的关键帧，并优化提示词。

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迭代式视频世界模型引导灵巧手：通过 33 步引导生成的视频序列拼出 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. 从机器人视角到仿真

Argon 观看一段真实机器人的遥操作过程，并构建数字孪生；它获取或创建仿真资产，使其外观及与灵巧手接触时的行为都与真实物体一致。Argon 通过迭代式视觉推理循环调整资产和场景，使仿真画面与接触事件匹配真实录制数据中的对应内容。

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左侧（真实视图）：同步的真实机器人摄像头画面；右侧（仿真）：由 Argon 重建并校准的 3D MuJoCo 场景。

提示词：

[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

给定一段人类执行操作任务的第一人称视角视频，Argon 会识别与任务相关的物体，用经过凸分解的复合网格替代凹形容器的粗略包围盒，并在 MuJoCo 接触物理仿真中，将人类手部轨迹重定向到一个配备 22 自由度五指手的 76 自由度双臂人形机器人上。

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从人类视角到仿真——从人类第一人称视角视频到灵巧人形机器人数字孪生：左侧为输入的人类第一人称视角 RGB 视频；右侧为同步的 500 Hz MuJoCo + Blender Cycles 多视角仿真运行画面。

提示词：

[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，开发者只需拍下一张粗略的纸笔草图——图中 Apollo 2 人形机器人沿抛物线投球，击倒桌上按 3-2-1 堆叠的箱子——就能让 Argon 自动构建 GPU 加速的 mujoco_warp 场景，合成保持平衡的 76 自由度全身及五指投掷轨迹，在闭环物理仿真中验证 6/6 目标均被击倒，并导出 Blender 4.5 Cycles 渲染结果。

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从草图到仿真——从手绘草图到灵巧人形机器人轨迹 & Cycles 渲染。

提示词：

[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 解锁物理理解能力

1. 回溯标注与数据质量

Argon 对灵巧操作具备精确的物理理解能力，能够根据视频进行细致的分段和文本标注。Argon 能识别停顿、抓握不稳和操作失误等低质量操作片段。给定一段双臂 ALOHA 2 机器人在 136 秒内将一张正方形纸折成折纸狐狸的俯视视频，Argon 会生成时间对齐的 4 层回溯标注体系：整段任务目标（G1）、6 个连续的语义子任务（G2）、17 个接触层面的原子操作（G3），以及自动质量控制（QC）重加权，在操作失误和重试期间保持任务进度不变。

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Argon 多粒度回溯标注 & 遥测平视显示界面。

提示词：

[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. 找出异重球

为了用机器人重现经典的 12 球天平谜题，Argon 构建了一个双臂 Franka FR3 MuJoCo 环境，其中包含 12 个外观完全相同的台球（其中一个实际上偏重或偏轻），以及一台带关节机构的 Roberval 双盘天平。随后，它编写了一个仅依赖视觉的闭环求解程序，通过双目 RGB 摄像头对球进行三角定位，在每次称量后通过视觉读取天平的倾斜状态，并在 3 次称量内将异重球分离到偏重（红色）或偏轻（蓝色）托盘中。

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找出异类球（纯视觉闭环求解器）：双臂 Franka FR3 在金色/青色秤盘上称量候选球，读取立体视觉中的天平倾斜情况，并在 3 次称量内将确认偏重的异类球单独放入红色托盘。

提示词：

# 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 秒双臂环形物分拣操作片段上进行评估时，Argon 围绕接触状态转换，反复检查同步的头部、左夹爪和右夹爪摄像头画面，生成密集的价值函数 / 任务进度预测，并将轨迹分解为 28 个片段，其进度斜率分别为正、零和负。

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价值函数：同步的三摄像头 UMI 回放（头部、左夹爪、右夹爪），与动态任务进度曲线和双臂操作片段标注保持同步。

提示词：

[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. 通过仿真改进行动

Argon 可以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。从一张真实桌面的 RGB 照片出发，Argon 自主构建经过校准的 MuJoCo 仿真孪生体、定义新任务，并合成闭环运动生成器，在仿真中收集 4,500+ 个经过域随机化的操作片段，将真实世界实体机器人的成功率从 56% 提升至 75%，并将完成时间从 35s 缩短至 24s。

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通过仿真改进行动：提升真实世界评估基准表现（成功率 56% → 75%，完成时间 35s → 24s）。

提示词：

[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. 视觉抓取放置任务的自我改进

在一台简单的 6 自由度机械臂上，Argon 通过试验和自我反思，迭代式地提升抓取放置任务的执行速度和放置精度。

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SO-101 视觉引导抓取放置——从迭代 1 → 2 → 3 的硬件迭代式自我改进：迭代 1（58.9s）、迭代 2（22.9s，放下时擦到盒子边缘）和迭代 3（17.4s，正中放下）。

提示词：

# 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 中以单条提示词复现机器人论文

每篇论文只需一条提示词，Argon 就会阅读机器人控制论文及其发布的所有检查点，将物理模型和闭环控制器（从 DeepLoco 中由 7 个网络组成的分层运动控制和 HUSKY 中的 50 Hz PPO 策略网络，到 Whole-Body MPC 中的 Web Worker iLQR）移植到自包含的 MuJoCo WebAssembly 浏览器实验环境中，并在无头 Chromium 中验证。

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五篇具有里程碑意义的腿式机器人 & 人形机器人论文在浏览器中实时运行（MuJoCo WASM）：Robotic Soccer OP3 1v1、DeepLoco、Massively Parallel Deep RL ANYmal C、Whole-Body MPC Go2 和 HUSKY G1 滑板（选自 Argon 复现的 68 个交互式浏览器沙盒）。

提示词：

[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 推进具身智能

Argon 将编程、视觉感知、物理仿真和具身机器人控制融为一体。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 不仅理解场景中的物体是什么，还理解它们如何与智能体进行物理交互，以完成需要全身灵巧协作的任务。Gemini 4 Argon 标志着加速迈向物理 AGI 的一个重要里程碑。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，解决复杂挑战。

开始使用

即日起，你就可以在 Google 的各个开发者平台上使用 Gemini 4 Argon 进行开发：

• Google AI Studio：面向 Google AI Ultra 订阅用户开放。
• Gemini API：可通过 AI Studio 中 Gemini API 的付费层级使用。在 Interactions API 中使用 gemini-4-argon，google-genai SDK 从首日起即提供支持。
• Google Antigravity：面向 Google AI Ultra 订阅用户开放。要开始开发，请在 Google Antigravity 中选择 Gemini 4 Argon（gemini-4-argon）作为当前模型，用于复杂规划、/goal 运行和多智能体编排。

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