Google DeepMind 发布 Gemini 4 Argon 机器人模型

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Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward physical AGI.

You can see this leap in capability on the Embodied Reasoning (ERQA) benchmark, which tests a model's ability to understand and solve physical, spatial problems in 3D environments. Using pure reasoning, Argon scores 87%, outperforming every other frontier model.

As a general-purpose physical reasoning model, Argon can be applied across the entire robotics lifecycle. In this post, we share representative prompts for Argon across four key areas:

Robot control → Mobile manipulation with MuJoCo practice, zero-shot robot control, and closed-loop steering of video world models; Real2Sim → Reconstructing physically calibrated simulation twins from robot camera feeds, egocentric human video, or hand-drawn paper sketches; Physical understanding → Agentic multimodal hindsight labeling including detailed understanding of dexterity, dense value functions and progress estimation; Research and self-improvement → Action improvement via simulation and iterative self-improvement on hardware

Unlocking robot control with Argon

1. Spot mobile manipulation: Putting in the trash bin with MuJoCo practice

Argon controls a Boston Dynamics Spot® quadruped (12-DoF legs + 6-DoF arm) to learn a new skill: stepping on a foot-pedal trash bin to throw away soda cans. Argon creates an articulated MuJoCo digital twin of a foot-pedal trash bin to practice its moves before stepping on the physical pedal and dropping soda cans into the open bin.

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2. Zero-shot control

Argon controls a simple robotic arm to place a fallen mug upright and put the crumpled napkin inside. Argon reasons with a visual coordinate harness and action primitives to perform this task over several turns.

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3. Omni control

Argon steers robots in the Gemini Omni World Model to rearrange five scattered Scrabble tiles (G, R, A, N, O) into a clean row spelling A - R - G - O - N. Instead of specifying one static prompt, Argon steers in a closed visual loop. It writes timestamped action prompts, inspecting intermediate rollout keyframes, and refining its prompts.

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Unlocking Real2Sim with Argon

1. Robot view to simulation

Argon views a real robot teleoperation episode and develops a digital twin; it obtains or creates simulation assets that look and behave like the real objects upon contact with the dexterous hands. Argon tunes the assets and scenes to match sim visuals and contact events with those in the real recorded data in an iterative visual-reasoning loop.

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2. Human view to simulation

Given an egocentric human video of a manipulation task, Argon identifies the task-relevant objects, replaces crude bounding boxes with compound convex-decomposed meshes for concave containers, and retargets the human hand trajectory onto a 76-DoF bimanual humanoid with 22-DoF five-fingered hands in MuJoCo contact physics.

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3. Sketch to simulation

With Argon, a developer can snap a photo of a rough pen-and-paper sketch—showing an Apollo 2 humanoid throwing a ball along a parabolic arc to topple a 3-2-1 stack of boxes on a table—and have Argon automatically construct the GPU-accelerated mujoco_warp scene, synthesize a balanced 76-DoF whole-body and five-finger throw trajectory, verify 6/6 target knockdown in closed-loop physics, and export a Blender 4.5 Cycles render.

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Unlocking physical understanding with Argon

1. Hindsight labeling and data quality

Argon has precise physical understanding of dexterous manipulation and can provide detailed segmentation and text labeling from video. Argon is able to identify poor manipulation segments such as pauses, unstable grasps and fumbles. Given an overhead video of a bimanual ALOHA 2 robot folding a square sheet of paper into an origami fox over 136 seconds, Argon produces a time-aligned 4-level hindsight annotation hierarchy: Episode Goal (G1), 6 contiguous Semantic Subtasks (G2), 17 contact-level Atomic Primitives (G3), and automated Quality Control (QC) reweighting that holds task progress flat during fumbles and retries.

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2. Find the odd ball

To recreate the classic 12-ball balance scale puzzle with robots, Argon builds a bimanual Franka FR3 MuJoCo environment with 12 visually identical pool balls (one secretly heavier or lighter) and an articulated Roberval two-pan balance scale. It then writes a vision-only closed-loop solver that triangulates the balls from stereo RGB cameras, visually reads the scale tilt after each weighing, and isolates the odd ball into the Heavy (Red) or Light (Blue) tray within 3 weighings.

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3. Value function

Argon is a general value function for robotics tasks. Evaluated on a 60-second bimanual ring-sorting episode from a human UMI dataset, Argon iteratively inspects synchronized head, left-gripper, and right-gripper camera feeds around contact transitions to generate dense value function / task progress predictions and decompose the trajectory into 28 segments with positive, neutral, and negative progress slopes.

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Unlocking research and self-improvement with Argon

1. Action improvement via simulation

Argon can generate new data in simulation to improve performance of generalist vision-language-action (VLA) models. Starting from a single RGB photo of the real tabletop, Argon autonomously builds a calibrated MuJoCo simulation twin, defines new tasks, and synthesizes a closed-loop motion generator to collect 4,500+ domain-randomized episodes in simulation—boosting real-world physical robot Success Rate from 56% to 75% and reducing completion time from 35s to 24s.

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2. Self-Improvement for visual pick and place

On a simple 6-DoF arm, Argon iteratively self-improves both execution speed and placement accuracy on a pick and place task via trial and self-reflection.

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3. One-prompt robotics paper reproductions in MuJoCo WebAssembly

With a single prompt per paper, Argon reads a robotics control manuscript and any released checkpoints, ports the physics model and closed-loop controller (from 7-network hierarchical locomotion in DeepLoco and 50 Hz PPO actors in HUSKY to Web Worker iLQR in Whole-Body MPC) into self-contained MuJoCo WebAssembly browser labs, and verifies them in headless Chromium.

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Advancing embodied intelligence with Argon

Argon bridges coding, visual perception, physics simulation, and embodied robot control. By reasoning natively over multi-view video, 3D geometry, contact mechanics, and proprioceptive telemetry, Argon understands not just what objects are in a scene, but how they physically interact with agents to accomplish whole-body dexterous tasks. Gemini 4 Argon marks an important milestone accelerating the path toward physical AGI. By building this core intelligence, our goal is to enable robotics that can work alongside humans to solve complex challenges.

Getting started

You can start building with Gemini 4 Argon today across Google’s developer platforms:

Optimizing a 200,000-line Python codebase overnight with Gemini 4 Argon